Month: January 2024

The evolution of technology in speech recognition has been marked by significant strides, but challenges like latency the time delay in processing spoken language, have continually impeded progress. This latency is especially pronounced in autoregressive models, which process speech sequentially, leading to delays. These delays are detrimental in real-time applications like live captioning or virtual assistants, where immediacy is key. Addressing this latency without compromising accuracy remains critical in advancing speech recognition technology.

A pioneering approach in speech recognition is developing a non-autoregressive model, a departure from traditional methods. This model, proposed by a team of researchers from Google Research, is designed to tackle the inherent latency issues found in existing systems. It utilizes large language models and leverages parallel processing, which processes speech segments simultaneously rather than sequentially. This similar processing approach is instrumental in reducing latency, offering a more fluid and responsive user experience.

The core of this innovative model is the fusion of the Universal Speech Model (USM) with the PaLM 2 language model. The USM, a robust model with 2 billion parameters, is designed for accurate speech recognition. It uses a vocabulary of 16,384-word pieces and employs a Connectionist Temporal Classification (CTC) decoder for parallel processing. The USM is trained on an extensive dataset, encompassing over 12 million hours of unlabeled audio and 28 billion sentences of text data, making it incredibly adept at handling multilingual inputs.

The PaLM 2 language model, known for its prowess in natural language processing, complements the USM. It’s trained on diverse data sources, including web documents and books, and employs a large 256,000 wordpiece vocabulary. The model stands out for its ability to score Automatic Speech Recognition (ASR) hypotheses using a prefix language model scoring mode. This method involves prompting the model with a fixed prefix—top hypotheses from previous segments—and scoring several suffix hypotheses for the current segment. 

In practice, the combined system processes long-form audio in 8-second chunks. As soon as the audio is available, the USM encodes it, and these segments are then relayed to the CTC decoder. The decoder forms a confusion network lattice encoding possible word pieces, which the PaLM 2 model scores. The system updates every 8 seconds, providing a near real-time response.

The performance of this model was rigorously evaluated across several languages and datasets, including YouTube captioning and the FLEURS test set. The results were remarkable. An average improvement of 10.8% in relative word error rate (WER) was observed on the multilingual FLEURS test set. For the YouTube captioning dataset, which presents a more challenging scenario, the model achieved an average improvement of 3.6% across all languages. These improvements are a testament to the model’s effectiveness in diverse languages and settings.

The study delved into various factors affecting the model’s performance. It explored the impact of language model size, ranging from 128 million to 340 billion parameters. It found that while larger models reduced sensitivity to fusion weight, the gains in WER might not offset the increasing inference costs. The optimal LLM scoring weight also shifted with model size, suggesting a balance between model complexity and computational efficiency.

In conclusion, this research presents a significant leap in speech recognition technology. Its highlights include:

A non-autoregressive model combining the USM and PaLM 2 for reduced latency.

Enhanced accuracy and speed, making it suitable for real-time applications.

Significant improvements in WER across multiple languages and datasets.

This model’s innovative approach to processing speech in parallel, coupled with its ability to handle multilingual inputs efficiently, makes it a promising solution for various real-world applications. The insights provided into system parameters and their effects on ASR efficacy add valuable knowledge to the field, paving the way for future advancements in speech recognition technology. 

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The post This AI Paper from Google Unveils a Groundbreaking Non-Autoregressive, LM-Fused ASR System for Superior Multilingual Speech Recognition appeared first on MarkTechPost.

With new releases and introductions in the field of Artificial Intelligence (AI), Large Language Models (LLMs) are advancing significantly. They are showcasing their incredible capability of generating and comprehending natural language. However, there are certain difficulties experienced by LLMs with an emphasis on English when managing non-English languages, especially those with constrained resources. Although the advent of generative multilingual LLMs is recognized, the language coverage of current models is considered inadequate.

An important milestone was reached when the XLM-R auto-encoding model was introduced with 278M parameters with language coverage from 100 languages to 534 languages. Even the Glot500-c corpora, which spans 534 languages from 47 language families, benefited the low-resource languages. Other effective strategies to address data scarcity include vocabulary extension and ongoing pretraining. 

The success of these models’ enormous language adoption serves as inspiration for more developments in this area. In a recent study, a team of researchers has specifically addressed the limitations of previous efforts that concentrated on small model sizes, with the goal of expanding the capabilities of LLMs to cover a wider range of languages. In order to improve contextual and linguistic relevance across a range of languages, the study discusses language adaptation strategies for LLMs with model parameters scaling up to 10 billion.

There are difficulties in adapting LLMs to low-resource languages, including problems with data sparsity, vocabulary peculiar to a given area, and linguistic variation. The team has suggested solutions, such as expanding vocabulary, continuing to train open LLMs, and utilizing adaption strategies like LoRA low-rank reparameterization.

A team of researchers associated with LMU Munich, Munich Center for Machine Learning, University of Helsinki, Instituto Superior Técnico (Lisbon ELLIS Unit), Instituto de Telecomunicações, and Unbabel has come up with a model called MaLA-500. MaLA-500 is a brand-new large language model designed to span a wide spectrum of 534 languages. Vocabulary expansion has been used in MaLA-500 training, along with ongoing LLaMA 2 pretraining using Glot500-c. The team has conducted an analysis using the SIB-200 dataset, which has shown that MaLA-500 performs better than currently available open LLMs with comparable or marginally bigger model sizes. It has achieved some amazing in-context learning outcomes, describing a model’s capacity to comprehend and produce language within a particular environment demonstrating its adaptability and significance in a range of linguistic contexts.

MaLA-500 is a great solution for the current LLMs’ inability to support low-resource languages. It exhibits state-of-the-art in-context learning results through unique approaches such as vocabulary extension and continuous pretraining. Vocabulary extension is the process of expanding the model’s vocabulary to cover a wider range of languages so that it can comprehend and produce material in a variety of languages.

In conclusion, this study is important because it increases the accessibility of language learning modules (LLMs), which makes them useful for a wide range of language-specific use cases, particularly for low-resource languages.

Check out the Paper and Model. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our 36k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group.

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The post Meet MaLA-500: A Novel Large Language Model Designed to Cover an Extensive Range of 534 Languages appeared first on MarkTechPost.

The evolution of language models is a critical component in the dynamic field of natural language processing. These models, essential for emulating human-like text comprehension and generation, are instrumental in various applications, from translation to conversational interfaces. The core challenge tackled in this area is refining model efficiency, particularly in managing lengthy data sequences. Traditional models, especially at the byte level, have historically struggled with this aspect, impacting their text processing and generation capabilities.

Now, models have typically employed subword or character-level tokenization, breaking down text into smaller, more manageable fragments. While useful, these techniques have their own set of limitations. They often need to improve in efficiently processing extensive sequences and more flexibility across linguistic and morphological structures.

Meet MambaByte, a groundbreaking byte-level language model developed by Cornell University researchers that revolutionizes this approach. It derives from the Mamba architecture, a state space model specifically tailored for sequence modeling. Its most striking feature is its operation directly on byte sequences, eliminating the need for traditional tokenization.

MambaByte truly stands out in its methodology. It harnesses the linear-time capabilities inherent in the Mamba architecture, enabling effective management of lengthy byte sequences. This innovative approach significantly reduces computational demands compared to conventional models, boosting efficiency and practicality for extensive language modeling tasks.

The performance of MambaByte is quite remarkable. MambaByte outperformed MegaByte consistently across all datasets. Furthermore, MambaByte couldn’t be trained for the full 80B bytes due to monetary constraints, but MambaByte beat MegaByte with 0.63× less compute and training data. Additionally, MambaByte-353M also exceeds byte-level Transformer and PerceiverAR. The results highlight MambaByte’s superior efficiency performance and its ability to achieve better results with less computational resources and training data compared to other leading models in the field.

Reflecting on MambaByte’s contributions, it’s clear that this model signifies a breakthrough in language modeling. Its proficiency in processing long-byte sequences without resorting to tokenization paves the way for more adaptable and potent natural language processing tools. The results hint at an exciting future where token-free language modeling could be pivotal in large-scale applications.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our 36k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group.

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The post Cornell Researchers Unveil MambaByte: A Game-Changing Language Model Outperforming MegaByte appeared first on MarkTechPost.

When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the time it takes to generate a single token, and throughput, defined by the number of tokens generated per second. Although a single request to the deployed endpoint would exhibit a throughput approximately equal to the inverse of model latency, this is not necessarily the case when multiple concurrent requests are simultaneously sent to the endpoint. Due to model serving techniques, such as client-side continuous batching of concurrent requests, latency and throughput have a complex relationship that varies significantly based on model architecture, serving configurations, instance type hardware, number of concurrent requests, and variations in input payloads such as number of input tokens and output tokens.
This post explores these relationships via a comprehensive benchmarking of LLMs available in Amazon SageMaker JumpStart, including Llama 2, Falcon, and Mistral variants. With SageMaker JumpStart, ML practitioners can choose from a broad selection of publicly available foundation models to deploy to dedicated Amazon SageMaker instances within a network-isolated environment. We provide theoretical principles on how accelerator specifications impact LLM benchmarking. We also demonstrate the impact of deploying multiple instances behind a single endpoint. Finally, we provide practical recommendations for tailoring the SageMaker JumpStart deployment process to align with your requirements on latency, throughput, cost, and constraints on available instance types. All the benchmarking results as well as recommendations are based on a versatile notebook that you can adapt to your use case.
Deployed endpoint benchmarking
The following figure shows the lowest latencies (left) and highest throughput (right) values for deployment configurations across a variety of model types and instance types. Importantly, each of these model deployments use default configurations as provided by SageMaker JumpStart given the desired model ID and instance type for deployment.

These latency and throughput values correspond to payloads with 256 input tokens and 256 output tokens. The lowest latency configuration limits model serving to a single concurrent request, and the highest throughput configuration maximizes the possible number of concurrent requests. As we can see in our benchmarking, increasing concurrent requests monotonically increases throughput with diminishing improvement for large concurrent requests. Additionally, models are fully sharded on the supported instance. For example, because the ml.g5.48xlarge instance has 8 GPUs, all SageMaker JumpStart models using this instance are sharded using tensor parallelism on all eight available accelerators.
We can note a few takeaways from this figure. First, not all models are supported on all instances; some smaller models, such as Falcon 7B, don’t support model sharding, whereas larger models have higher compute resource requirements. Second, as sharding increases, performance typically improves, but may not necessarily improve for small models. This is because small models such as 7B and 13B incur a substantial communication overhead when sharded across too many accelerators. We discuss this in more depth later. Finally, ml.p4d.24xlarge instances tend to have significantly better throughput due to memory bandwidth improvements of A100 over A10G GPUs. As we discuss later, the decision to use a particular instance type depends on your deployment requirements, including latency, throughput, and cost constraints.
How can you obtain these lowest latency and highest throughput configuration values? Let’s start by plotting latency vs. throughput for a Llama 2 7B endpoint on an ml.g5.12xlarge instance for a payload with 256 input tokens and 256 output tokens, as seen in the following curve. A similar curve exists for every deployed LLM endpoint.

As concurrency increases, throughput and latency also monotonically increase. Therefore, the lowest latency point occurs at a concurrent request value of 1, and you can cost-effectively increase system throughput by increasing concurrent requests. There exists a distinct “knee” in this curve, where it’s obvious that the throughput gains associated with additional concurrency don’t outweigh the associated increase in latency. The exact location of this knee is use case-specific; some practitioners may define the knee at the point where a pre-specified latency requirement is exceeded (for example, 100 ms/token), whereas others may use load test benchmarks and queueing theory methods like the half-latency rule, and others may use theoretical accelerator specifications.
We also note that the maximum number of concurrent requests is limited. In the preceding figure, the line trace ends with 192 concurrent requests. The source of this limitation is the SageMaker invocation timeout limit, where SageMaker endpoints timeout an invocation response after 60 seconds. This setting is account-specific and not configurable for an individual endpoint. For LLMs, generating a large number of output tokens can take seconds or even minutes. Therefore, large input or output payloads can cause the invocation requests to fail. Furthermore, if the number of concurrent requests is very large, then many requests will experience large queue times, driving this 60-second timeout limit. For the purpose of this study, we use the timeout limit to define the maximum throughput possible for a model deployment. Importantly, although a SageMaker endpoint may handle a large number of concurrent requests without observing an invocation response timeout, you may want to define maximum concurrent requests with respect to the knee in the latency-throughput curve. This is likely the point at which you start to consider horizontal scaling, where a single endpoint provisions multiple instances with model replicas and load balances incoming requests between the replicas, to support more concurrent requests.
Taking this one step further, the following table contains benchmarking results for different configurations for the Llama 2 7B model, including different number of input and output tokens, instance types, and number of concurrent requests. Note that the preceding figure only plots a single row of this table.

.
Throughput (tokens/sec)
Latency (ms/token)

Concurrent Requests
1
2
4
8
16
32
64
128
256
512
1
2
4
8
16
32
64
128
256
512

Number of total tokens: 512,    Number of output tokens: 256

ml.g5.2xlarge
30
54
115
208
343
475
486
—
—
—
33
33
35
39
48
97
159
—
—
—

ml.g5.12xlarge
59
117
223
406
616
866
1098
1214
—
—
17
17
18
20
27
38
60
112
—
—

ml.g5.48xlarge
56
108
202
366
522
660
707
804
—
—
18
18
19
22
32
50
101
171
—
—

ml.p4d.24xlarge
49
85
178
353
654
1079
1544
2312
2905
2944
21
23
22
23
26
31
44
58
92
165

Number of total tokens: 4096,    Number of output tokens: 256

ml.g5.2xlarge
20
36
48
49
—
—
—
—
—
—
48
57
104
170
—
—
—
—
—
—

ml.g5.12xlarge
33
58
90
123
142
—
—
—
—
—
31
34
48
73
132
—
—
—
—
—

ml.g5.48xlarge
31
48
66
82
—
—
—
—
—
—
31
43
68
120
—
—
—
—
—
—

ml.p4d.24xlarge
39
73
124
202
278
290
—
—
—
—
26
27
33
43
66
107
—
—
—
—

We observe some additional patterns in this data. When increasing context size, latency increases and throughput decreases. For instance, on ml.g5.2xlarge with a concurrency of 1, throughput is 30 tokens/sec when the number of total tokens is 512, vs. 20 tokens/sec if the number of total tokens is 4,096. This is because it takes more time to process the larger input. We can also see that increasing GPU capability and sharding impacts the maximum throughput and maximum supported concurrent requests. The table shows that Llama 2 7B has notably different maximum throughput values for different instance types, and these maximum throughput values occur at different values of concurrent requests. These characteristics would drive an ML practitioner to justify the cost of one instance over another. For example, given a low latency requirement, the practitioner might select an ml.g5.12xlarge instance (4 A10G GPUs) over an ml.g5.2xlarge instance (1 A10G GPU). If given a high throughput requirement, the use of an ml.p4d.24xlarge instance (8 A100 GPUs) with full sharding would only be justified under high concurrency. Note, however, that it’s often beneficial to instead load multiple inference components of a 7B model on a single ml.p4d.24xlarge instance; such multi-model support is discussed later in this post.
The preceding observations were made for the Llama 2 7B model. However, similar patterns remain true for other models as well. A primary takeaway is that latency and throughput performance numbers are dependent on payload, instance type, and number of concurrent requests, so you will need to find the ideal configuration for your specific application. To generate the preceding numbers for your use case, you can run the linked notebook, where you can configure this load test analysis for your model, instance type, and payload.
Making sense of accelerator specifications
Selecting suitable hardware for LLM inference relies heavily on specific use cases, user experience goals, and the chosen LLM. This section attempts to create an understanding of the knee in the latency-throughput curve with respect to high-level principles based on accelerator specifications. These principles alone don’t suffice to make a decision: real benchmarks are necessary. The term device is used here to encompass all ML hardware accelerators. We assert the knee in the latency-throughput curve is driven by one of two factors:

The accelerator has exhausted memory to cache KV matrices, so subsequent requests are queued
The accelerator still has spare memory for the KV cache, but is using a large enough batch size that processing time is driven by compute operation latency rather than memory bandwidth

We typically prefer to be limited by the second factor because this implies the accelerator resources are saturated. Basically, you are maximizing the resources you payed for. Let’s explore this assertion in greater detail.
KV caching and device memory
Standard transformer attention mechanisms compute attention for each new token against all previous tokens. Most modern ML servers cache attention keys and values in device memory (DRAM) to avoid re-computation at every step. This is called this the KV cache, and it grows with batch size and sequence length. It defines how many user requests can be served in parallel and will determine the knee in the latency-throughput curve if the compute-bound regime in the second scenario mentioned earlier is not yet met, given the available DRAM. The following formula is a rough approximation for the maximum KV cache size.

In this formula, B is batch size and N is number of accelerators. For example, the Llama 2 7B model in FP16 (2 bytes/parameter) served on an A10G GPU (24 GB DRAM) consumes approximately 14 GB, leaving 10 GB for the KV cache. Plugging in the model’s full context length (N = 4096) and remaining parameters (n_layers=32, n_kv_attention_heads=32, and d_attention_head=128), this expression shows we are limited to serving a batch size of four users in parallel due to DRAM constraints. If you observe the corresponding benchmarks in the previous table, this is a good approximation for the observed knee in this latency-throughput curve. Methods such as grouped query attention (GQA) can reduce the KV cache size, in GQA’s case by the same factor it reduces the number of KV heads.
Arithmetic intensity and device memory bandwidth
The growth in the computational power of ML accelerators has outpaced their memory bandwidth, meaning they can perform many more computations on each byte of data in the amount of time it takes to access that byte.
The arithmetic intensity, or the ratio of compute operations to memory accesses, for an operation determines if it is limited by memory bandwidth or compute capacity on the selected hardware. For example, an A10G GPU (g5 instance type family) with 70 TFLOPS FP16 and 600 GB/sec bandwidth can compute approximately 116 ops/byte. An A100 GPU (p4d instance type family) can compute approximately 208 ops/byte. If the arithmetic intensity for a transformer model is under that value, it is memory-bound; if it is above, it is compute-bound. The attention mechanism for Llama 2 7B requires 62 ops/byte for batch size 1 (for an explanation, see A guide to LLM inference and performance), which means it is memory-bound. When the attention mechanism is memory-bound, expensive FLOPS are left unutilized.
There are two ways to better utilize the accelerator and increase arithmetic intensity: reduce the required memory accesses for the operation (this is what FlashAttention focuses on) or increase the batch size. However, we might not be able to increase our batch size enough to reach a compute-bound regime if our DRAM is too small to hold the corresponding KV cache. A crude approximation of the critical batch size B* that separates compute-bound from memory-bound regimes for standard GPT decoder inference is described by the following expression, where A_mb is the accelerator memory bandwidth, A_f is accelerator FLOPS, and N is the number of accelerators. This critical batch size can be derived by finding where memory access time equals computation time. Refer to this blog post to understand Equation 2 and its assumptions in greater detail.

This is the same ops/byte ratio we previously calculated for A10G, so the critical batch size on this GPU is 116. One way to approach this theoretical, critical batch size is to increase model sharding and split the cache across more N accelerators. This effectively increases the KV cache capacity as well as the memory-bound batch size.
Another benefit of model sharding is splitting model parameter and data loading work across N accelerators. This type of sharding is a type of model parallelism also referred to as tensor parallelism. Naively, there is N times the memory bandwidth and compute power in aggregate. Assuming no overhead of any kind (communication, software, and so on), this would decrease decoding latency per token by N if we are memory-bound, because token decoding latency in this regime is bound by the time it takes to load the model weights and cache. In real life, however, increasing the degree of sharding results in increased communication between devices to share intermediate activations at every model layer. This communication speed is limited by the device interconnect bandwidth. It’s difficult to estimate its impact precisely (for details, see Model parallelism), but this can eventually stop yielding benefits or deteriorate performance — this is especially true for smaller models, because smaller data transfers lead to lower transfer rates.
To compare ML accelerators based on their specs, we recommend the following. First, calculate the approximate critical batch size for each accelerator type according to the second equation and the KV cache size for the critical batch size according to the first equation. You can then use the available DRAM on the accelerator to calculate the minimum number of accelerators required to fit the KV cache and model parameters. If deciding between multiple accelerators, prioritize accelerators in order of lowest cost per GB/sec of memory bandwidth. Finally, benchmark these configurations and verify what is the best cost/token for your upper bound of desired latency.
Select an endpoint deployment configuration
Many LLMs distributed by SageMaker JumpStart use the text-generation-inference (TGI) SageMaker container for model serving. The following table discusses how to adjust a variety of model serving parameters to either affect model serving which impacts the latency-throughput curve or protect the endpoint against requests that would overload the endpoint. These are the primary parameters you can use to configure your endpoint deployment for your use case. Unless otherwise specified, we use default text generation payload parameters and TGI environment variables.

Environment Variable
Description
SageMaker JumpStart Default Value

Model serving configurations
.
.

MAX_BATCH_PREFILL_TOKENS
Limits the number of tokens in the prefill operation. This operation generates the KV cache for a new input prompt sequence. It is memory intensive and compute bound, so this value caps the number of tokens allowed in a single prefill operation. Decoding steps for other queries pause while prefill is occurring.
4096 (TGI default) or model-specific maximum supported context length (SageMaker JumpStart provided), whichever is greater.

MAX_BATCH_TOTAL_TOKENS
Controls the maximum number of tokens to include within a batch during decoding, or a single forward pass through the model. Ideally, this is set to maximize the usage of all available hardware.
Not specified (TGI default). TGI will set this value with respect to remaining CUDA memory during model warm up.

SM_NUM_GPUS
The number of shards to use. That is, the number of GPUs used to run the model using tensor parallelism.
Instance dependent (SageMaker JumpStart provided). For each supported instance for a given model, SageMaker JumpStart provides the best setting for tensor parallelism.

Configurations to guard your endpoint (set these for your use case)
.
.

MAX_TOTAL_TOKENS
This caps the memory budget of a single client request by limiting the number of tokens in the input sequence plus the number of tokens in the output sequence (the max_new_tokens payload parameter).
Model-specific maximum supported context length. For example, 4096 for Llama 2.

MAX_INPUT_LENGTH
Identifies the maximum allowed number of tokens in the input sequence for a single client request. Things to consider when increasing this value include: longer input sequences require more memory, which affects continuous batching, and many models have a supported context length that should not be exceeded.
Model-specific maximum supported context length. For example, 4095 for Llama 2.

MAX_CONCURRENT_REQUESTS
The maximum number of concurrent requests allowed by the deployed endpoint. New requests beyond this limit will immediately raise a model overloaded error to prevent poor latency for the current processing requests.
128 (TGI default). This setting allows you to obtain high throughput for a variety of use cases, but you should pin as appropriate to mitigate SageMaker invocation timeout errors.

The TGI server uses continuous batching, which dynamically batches concurrent requests together to share a single model inference forward pass. There are two types of forward passes: prefill and decode. Each new request must run a single prefill forward pass to populate the KV cache for the input sequence tokens. After the KV cache is populated, a decode forward pass performs a single next-token prediction for all batched requests, which is iteratively repeated to produce the output sequence. As new requests are sent to the server, the next decode step must wait so the prefill step can run for the new requests. This must occur before those new requests are included in subsequent continuously batched decode steps. Due to hardware constraints, the continuous batching used for decoding may not include all requests. At this point, requests enter a processing queue and inference latency starts to significantly increase with only minor throughput gain.
It’s possible to separate LLM latency benchmarking analyses into prefill latency, decode latency, and queue latency. The time consumed by each of these components is fundamentally different in nature: prefill is a one-time computation, decoding occurs one time for each token in the output sequence, and queueing involves server batching processes. When multiple concurrent requests are being processed, it becomes difficult to disentangle the latencies from each of these components because the latency experienced by any given client request involves queue latencies driven by the need to prefill new concurrent requests as well as queue latencies driven by the inclusion of the request in batch decoding processes. For this reason, this post focuses on end-to-end processing latency. The knee in the latency-throughput curve occurs at the point of saturation where queue latencies start to significantly increase. This phenomenon occurs for any model inference server and is driven by accelerator specifications.
Common requirements during deployment include satisfying a minimum required throughput, maximum allowed latency, maximum cost per hour, and maximum cost to generate 1 million tokens. You should condition these requirements on payloads that represent end-user requests. A design to meet these requirements should consider many factors, including the specific model architecture, size of the model, instance types, and instance count (horizontal scaling). In the following sections, we focus on deploying endpoints to minimize latency, maximize throughput, and minimize cost. This analysis considers 512 total tokens and 256 output tokens.
Minimize latency
Latency is an important requirement in many real-time use cases. In the following table, we look at minimum latency for each model and each instance type. You can achieve minimum latency by setting MAX_CONCURRENT_REQUESTS = 1.

Minimum Latency (ms/token)

Model ID
ml.g5.2xlarge
ml.g5.12xlarge
ml.g5.48xlarge
ml.p4d.24xlarge
ml.p4de.24xlarge

Llama 2 7B
33
17
18
20
—

Llama 2 7B Chat
33
17
18
20
—

Llama 2 13B
—
22
23
23
—

Llama 2 13B Chat
—
23
23
23
—

Llama 2 70B
—
—
57
43
—

Llama 2 70B Chat
—
—
57
45
—

Mistral 7B
35
—
—
—
—

Mistral 7B Instruct
35
—
—
—
—

Mixtral 8x7B
—
—
33
27
—

Falcon 7B
33
—
—
—
—

Falcon 7B Instruct
33
—
—
—
—

Falcon 40B
—
53
33
27
—

Falcon 40B Instruct
—
53
33
28
—

Falcon 180B
—
—
—
—
42

Falcon 180B Chat
—
—
—
—
42

To achieve minimum latency for a model, you can use the following code while substituting your desired model ID and instance type:

from sagemaker.jumpstart.model import JumpStartModel

model = JumpStartModel(
model_id=”meta-textgeneration-llama-2-7b”,
model_version=”3.*”,
instance_type=”ml.g5.12xlarge”,
env={
“MAX_CONCURRENT_REQUESTS”: “1”,
“MAX_INPUT_TOKENS”: “256”,
“MAX_TOTAL_TOKENS”: “512”,
},
)
predictor = model.deploy(accept_eula=False) # Change EULA acceptance to True

Note that the latency numbers change depending on the number of input and output tokens. However, the deployment process remains the same except the environment variables MAX_INPUT_TOKENS and MAX_TOTAL_TOKENS. Here, these environment variables are set to help guarantee endpoint latency requirements because larger input sequences may violate the latency requirement. Note that SageMaker JumpStart already provides the other optimal environment variables when selecting instance type; for instance, using ml.g5.12xlarge will set SM_NUM_GPUS to 4 in the model environment.
Maximize throughput
In this section, we maximize the number of generated tokens per second. This is typically achieved at the maximum valid concurrent requests for the model and the instance type. In the following table, we report the throughput achieved at the largest concurrent request value achieved before encountering a SageMaker invocation timeout for any request.

Maximum Throughput (tokens/sec), Concurrent Requests

Model ID
ml.g5.2xlarge
ml.g5.12xlarge
ml.g5.48xlarge
ml.p4d.24xlarge
ml.p4de.24xlarge

Llama 2 7B
486 (64)
1214 (128)
804 (128)
2945 (512)
—

Llama 2 7B Chat
493 (64)
1207 (128)
932 (128)
3012 (512)
—

Llama 2 13B
—
787 (128)
496 (64)
3245 (512)
—

Llama 2 13B Chat
—
782 (128)
505 (64)
3310 (512)
—

Llama 2 70B
—
—
124 (16)
1585 (256)
—

Llama 2 70B Chat
—
—
114 (16)
1546 (256)
—

Mistral 7B
947 (64)
—
—
—
—

Mistral 7B Instruct
986 (128)
—
—
—
—

Mixtral 8x7B
—
—
701 (128)
3196 (512)
—

Falcon 7B
1340 (128)
—
—
—
—

Falcon 7B Instruct
1313 (128)
—
—
—
—

Falcon 40B
—
244 (32)
382 (64)
2699 (512)
—

Falcon 40B Instruct
—
245 (32)
415 (64)
2675 (512)
—

Falcon 180B
—
—
—
—
1100 (128)

Falcon 180B Chat
—
—
—
—
1081 (128)

To achieve maximum throughput for a model, you can use the following code:

from sagemaker.jumpstart.model import JumpStartModel

model = JumpStartModel(
model_id=”meta-textgeneration-llama-2-7b”,
model_version=”3.*”,
instance_type=”ml.g5.12xlarge”,
env={
“MAX_CONCURRENT_REQUESTS”: “128”, # For your application, identify it from the benchmarking table with the maximum feasible concurrent requests.
“MAX_INPUT_TOKENS”: “256”,
“MAX_TOTAL_TOKENS”: “512”,
},
)
predictor = model.deploy(accept_eula=False) # Change EULA acceptance to True

Note that the maximum number of concurrent requests depends on the model type, instance type, maximum number of input tokens, and maximum number of output tokens. Therefore, you should set these parameters before setting MAX_CONCURRENT_REQUESTS.
Also note that a user interested in minimizing latency is often at odds with a user interested in maximizing throughput. The former is interested in real-time responses, whereas the latter is interested in batch processing such that the endpoint queue is always saturated, thereby minimizing processing downtime. Users who want to maximize throughput conditioned on latency requirements are often interested in operating at the knee in the latency-throughput curve.
Minimize cost
The first option to minimize cost involves minimizing cost per hour. With this, you can deploy a selected model on the SageMaker instance with the lowest cost per hour. For real-time pricing of SageMaker instances, refer to Amazon SageMaker pricing. In general, the default instance type for SageMaker JumpStart LLMs is the lowest-cost deployment option.
The second option to minimize cost involves minimizing the cost to generate 1 million tokens. This is a simple transformation of the table we discussed earlier to maximize throughput, where you can first compute the time it takes in hours to generate 1 million tokens (1e6 / throughput / 3600). You can then multiply this time to generate 1 million tokens with the price per hour of the specified SageMaker instance.
Note that instances with the lowest cost per hour aren’t the same as instances with the lowest cost to generate 1 million tokens. For instance, if the invocation requests are sporadic, an instance with the lowest cost per hour might be optimal, whereas in the throttling scenarios, the lowest cost to generate a million tokens might be more appropriate.
Tensor parallel vs. multi-model trade-off
In all previous analyses, we considered deploying a single model replica with a tensor parallel degree equal to the number of GPUs on the deployment instance type. This is the default SageMaker JumpStart behavior. However, as previously noted, sharding a model can improve model latency and throughput only up to a certain limit, beyond which inter-device communication requirements dominate computation time. This implies that it’s often beneficial to deploy multiple models with a lower tensor parallel degree on a single instance rather than a single model with a higher tensor parallel degree.
Here, we deploy Llama 2 7B and 13B endpoints on ml.p4d.24xlarge instances with tensor parallel (TP) degrees of 1, 2, 4, and 8. For clarity in model behavior, each of these endpoints only load a single model.

.
Throughput (tokens/sec)
Latency (ms/token)

Concurrent Requests
1
2
4
8
16
32
64
128
256
512
1
2
4
8
16
32
64
128
256
512

TP Degree
Llama 2 13B

1
38
74
147
278
443
612
683
722
—
—
26
27
27
29
37
45
87
174
—
—

2
49
92
183
351
604
985
1435
1686
1726
—
21
22
22
22
25
32
46
91
159
—

4
46
94
181
343
655
1073
1796
2408
2764
2819
23
21
21
24
25
30
37
57
111
172

8
44
86
158
311
552
1015
1654
2450
3087
3180
22
24
26
26
29
36
42
57
95
152

.
Llama 2 7B

1
62
121
237
439
778
1122
1569
1773
1775
—
16
16
17
18
22
28
43
88
151
—

2
62
122
239
458
780
1328
1773
2440
2730
2811
16
16
17
18
21
25
38
56
103
182

4
60
106
211
420
781
1230
2206
3040
3489
3752
17
19
20
18
22
27
31
45
82
132

8
49
97
179
333
612
1081
1652
2292
2963
3004
22
20
24
26
27
33
41
65
108
167

Our previous analyses already showed significant throughput advantages on ml.p4d.24xlarge instances, which often translates to better performance in terms of cost to generate 1 million tokens over the g5 instance family under high concurrent request load conditions. This analysis clearly demonstrates that you should consider the trade-off between model sharding and model replication within a single instance; that is, a fully sharded model is not typically the best use of  ml.p4d.24xlarge compute resources for 7B and 13B model families. In fact, for the 7B model family, you obtain the best throughput for a single model replica with a tensor parallel degree of 4 instead of 8.
From here, you can extrapolate that the highest throughput configuration for the 7B model involves a tensor parallel degree of 1 with eight model replicas, and the highest throughput configuration for the 13B model is likely a tensor parallel degree of 2 with four model replicas. To learn more about how to accomplish this, refer to Reduce model deployment costs by 50% on average using the latest features of Amazon SageMaker, which demonstrates the use of inference component-based endpoints. Due to load balancing techniques, server routing, and sharing of CPU resources, you might not fully achieve throughput improvements exactly equal to the number of replicas times the throughput for a single replica.
Horizontal scaling
As observed earlier, each endpoint deployment has a limitation on the number of concurrent requests depending on the number of input and output tokens as well as the instance type. If this doesn’t meet your throughput or concurrent request requirement, you can scale up to utilize more than one instance behind the deployed endpoint. SageMaker automatically performs load balancing of queries between instances. For example, the following code deploys an endpoint supported by three instances:

model = JumpStartModel(
model_id=”meta-textgeneration-llama-2-7b”,
model_version=”3.*”,
instance_type=”ml.g5.2xlarge”,
)
predictor = model.deploy(
accept_eula=False, # Change EULA acceptance to True
initial_instance_count = 3,
)

The following table shows the throughput gain as a factor of number of instances for the Llama 2 7B model.

.
.
Throughput (tokens/sec)
Latency (ms/token)

.
Concurrent Requests
1
2
4
8
16
32
64
128
1
2
4
8
16
32
64
128

Instance Count
Instance Type
Number of total tokens: 512, Number of output tokens: 256

1
ml.g5.2xlarge
30
60
115
210
351
484
492
—
32
33
34
37
45
93
160
—

2
ml.g5.2xlarge
30
60
115
221
400
642
922
949
32
33
34
37
42
53
94
167

3
ml.g5.2xlarge
30
60
118
228
421
731
1170
1400
32
33
34
36
39
47
57
110

Notably, the knee in the latency-throughput curve shifts to the right because higher instance counts can handle larger numbers of concurrent requests within the multi-instance endpoint. For this table, the concurrent request value is for the entire endpoint, not the number of concurrent requests that each individual instance receives.
You can also use autoscaling, a feature to monitor your workloads and dynamically adjust the capacity to maintain steady and predictable performance at the possible lowest cost. This is beyond the scope of this post. To learn more about autoscaling, refer to Configuring autoscaling inference endpoints in Amazon SageMaker.
Invoke endpoint with concurrent requests
Let’s suppose you have a large batch of queries that you would like to use to generate responses from a deployed model under high throughput conditions. For example, in the following code block, we compile a list of 1,000 payloads, with each payload requesting the generation of 100 tokens. In all, we are requesting the generation of 100,000 tokens.

payload = {
“inputs”: “I believe the meaning of life is to “,
“parameters”: {“max_new_tokens”: 100, “details”: True},
}
total_requests = 1000
payloads = [payload,] * total_requests

When sending a large number of requests to the SageMaker runtime API, you may experience throttling errors. To mitigate this, you can create a custom SageMaker runtime client that increases the number of retry attempts. You can provide the resulting SageMaker session object to either the JumpStartModel constructor or sagemaker.predictor.retrieve_default if you would like to attach a new predictor to an already deployed endpoint. In the following code, we use this session object when deploying a Llama 2 model with default SageMaker JumpStart configurations:

import boto3
from botocore.config import Config
from sagemaker.session import Session
from sagemaker.jumpstart.model import JumpStartModel

sagemaker_session = Session(
sagemaker_runtime_client=boto3.client(
“sagemaker-runtime”,
config=Config(connect_timeout=10, retries={“mode”: “standard”, “total_max_attempts”: 20}),
)
)
model = JumpStartModel(
model_id=”meta-textgeneration-llama-2-7b”,
model_version=”3.*”,
sagemaker_session=sagemaker_session
)
predictor = model.deploy(accept_eula=False) # Change EULA acceptance to True

This deployed endpoint has MAX_CONCURRENT_REQUESTS = 128 by default. In the following block, we use the concurrent futures library to iterate over invoking the endpoint for all payloads with 128 worker threads. At most, the endpoint will process 128 concurrent requests, and whenever a request returns a response, the executor will immediately send a new request to the endpoint.

import time
from concurrent import futures

concurrent_requests = 128

time_start = time.time()
with futures.ThreadPoolExecutor(max_workers=concurrent_requests) as executor:
responses = list(executor.map(predictor.predict, payloads))

total_tokens = sum([response[0][“details”][“generated_tokens”] for response in responses])
token_throughput = total_tokens / (time.time() – time_start)

This results in generating 100,000 total tokens with a throughput of 1255 tokens/sec on a single ml.g5.2xlarge instance. This takes approximately 80 seconds to process.
Note that this throughput value is notably different than the maximum throughput for Llama 2 7B on ml.g5.2xlarge in the previous tables of this post (486 tokens/sec at 64 concurrent requests). This is because the input payload uses 8 tokens instead of 256, the output token count is 100 instead of 256, and the smaller token counts allow for 128 concurrent requests. This is a final reminder that all latency and throughput numbers are payload dependent! Changing payload token counts will affect batching processes during model serving, which will in turn affect the emergent prefill, decode, and queue times for your application.
Conclusion
In this post, we presented benchmarking of SageMaker JumpStart LLMs, including Llama 2, Mistral, and Falcon. We also presented a guide to optimize latency, throughput, and cost for your endpoint deployment configuration. You can get started by running the associated notebook to benchmark your use case.

About the Authors
 Dr. Kyle Ulrich is an Applied Scientist with the Amazon SageMaker JumpStart team. His research interests include scalable machine learning algorithms, computer vision, time series, Bayesian non-parametrics, and Gaussian processes. His PhD is from Duke University and he has published papers in NeurIPS, Cell, and Neuron.
Dr. Vivek Madan is an Applied Scientist with the Amazon SageMaker JumpStart team. He got his PhD from University of Illinois at Urbana-Champaign and was a Post Doctoral Researcher at Georgia Tech. He is an active researcher in machine learning and algorithm design and has published papers in EMNLP, ICLR, COLT, FOCS, and SODA conferences.
Dr. Ashish Khetan is a Senior Applied Scientist with Amazon SageMaker JumpStart and helps develop machine learning algorithms. He got his PhD from University of Illinois Urbana-Champaign. He is an active researcher in machine learning and statistical inference, and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.
João Moura is a Senior AI/ML Specialist Solutions Architect at AWS. João helps AWS customers – from small startups to large enterprises – train and deploy large models efficiently, and more broadly build ML platforms on AWS.

In the evolving landscape of artificial intelligence and natural language processing, utilizing large language models (LLMs) has become increasingly prevalent. However, one of the challenges that persist in this domain is enabling these models to engage in role-play effectively. This work requires a deep understanding of language and an ability to embody diverse characters consistently. The researchers from Alibaba address this challenge by introducing DITTO, a novel self-alignment method that significantly enhances the role-play capabilities of LLMs.

This study aims to solve the core problem of the limited role-playing proficiency of open-source LLMs compared to their proprietary counterparts. Traditional methods have tried to mimic the role-playing capabilities of models like GPT-4 using less powerful open-source models. These efforts, however, have not fully realized the potential of role-play in LLMs, often struggling to maintain a consistent role identity and to provide accurate, role-specific knowledge in multi-turn role-play conversations.

This research proposes a unique approach: LLMs are perceived as amalgamations of various characters owing to their training on extensive corpora that include a wide range of character experiences, events, personalities, and dialogues. The DITTO method leverages this inherent character knowledge within LLMs, enabling them to simulate role-play dialogues effectively. This process views role-play as a variant of reading comprehension, where the LLM aligns itself to different characters based on provided attributes and profiles.

DITTO’s methodology collects character profiles from open-source knowledge bases like Wikidata and Wikipedia. This foundational step involves compiling comprehensive profiles for many characters, setting the stage for the subsequent dialogue simulation phase. In this phase, role-play dialogues are simulated through a sequence of reading comprehension tasks, where queries relevant to the characters’ backgrounds are generated and responded to by the LLM. This approach allows the LLM to access and utilize its intrinsic knowledge about numerous characters, fostering a more authentic and varied role-play experience.

The method was tested using open-source LLMs such as Llama-2, MPT, and OpenLLaMA. Compared to existing open-source role-play baselines, the fused model exhibited superior performance across various benchmarks, including reasoning, commonsense, and code generation tasks. DITTO demonstrated an ability to maintain a consistent role identity and provide accurate, role-specific knowledge in multi-turn role-play conversations, outperforming previous approaches and showcasing performance levels on par with advanced proprietary chatbots.

In conclusion, this study presents a significant advancement in the field of LLMs. The introduction of DITTO marks a pivotal step in enabling open-source LLMs to achieve a level of role-playing proficiency previously seen only in proprietary models. This method enhances the role-play capabilities of LLMs and opens new possibilities for their application in various interactive and engaging scenarios. The findings from this research underscore the potential of leveraging the inherent capabilities of LLMs in creative and innovative ways, paving the way for further advancements in natural language processing and artificial intelligence.

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The post Alibaba Researchers Introduce Ditto: A Revolutionary Self-Alignment Method to Enhance Role-Play in Large Language Models Beyond GPT-4 Standards appeared first on MarkTechPost.

Language models (LMs) often struggle with reasoning tasks like math or coding, particularly in low-resource languages. This challenge arises because LMs are primarily trained on data from a few high-resource languages, leaving low-resource languages underrepresented. 

Previously, researchers have addressed this by continually training English-centric LMs on target languages. However, this method is difficult to scale across many languages due to the need for specific training data for each language. This issue could be more problematic for specialized LMs like MetaMath and Orca 2, which have undergone domain-specific adaptation primarily in English.

Researchers at KAIST and the University of Washington have introduced ‘LANGBRIDGE, ‘ a novel method for adapting LMs to multilingual reasoning tasks without requiring explicit multilingual training data. LANGBRIDGE combines two specialized models: one adept at understanding multiple languages (such as an mT5 encoder) and another focused on reasoning (like Orca 2). By introducing minimal trainable parameters between them, LANGBRIDGE effectively connects those models. 

Importantly, their approach doesn’t require multilingual supervision and relies solely on English data while still generalizing to multiple languages during testing, similar to zero-shot cross-lingual transfer. They demonstrate LANGBRIDGE’s effectiveness on LMs specialized in mathematical reasoning, coding, and logical reasoning. Empirical results show significant improvements in multilingual reasoning performance. 

Even though it’s trained only on English data, LANGBRIDGE significantly boosts language models’ performance on low-resource languages across various reasoning tasks like mathematics, coding, and logic. Their analysis indicates that the success of LANGBRIDGE is due to the language-agnostic nature of multilingual representations inspired by multimodal literature. For instance, applying LANGBRIDGE to MetaMath-13B using the mT5-XXL encoder boosts average accuracy on MGSM from 40.5% to 55.8%, matching the performance of PaLM540B at 51.3%.

They hypothesize that LANGBRIDGE’s effectiveness lies in the language-agnostic nature of multilingual representations. By mapping these representations to the LMs’ input space, the LM can grasp their semantics, making the specific language of the input irrelevant. Empirical analysis using techniques like principal component analysis (PCA) and qualitative methods supports their hypothesis.

Although multilingual representations are generally language-agnostic, previous research suggests room for improvement. While LANGBRIDGE has the potential to generalize to all languages supported by the multilingual encoder, its effectiveness in enhancing the reasoning capability of a specific language depends on two main factors: the initial proficiency of the language model in that language and the proficiency of the encoder model in that language.

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The post Researchers from KAIST and the University of Washington have introduced ‘LANGBRIDGE’: A Zero-Shot AI Approach to Adapt Language Models for Multilingual Reasoning Tasks without Multilingual Supervision appeared first on MarkTechPost.

Recent advances in language models showcase impressive zero-shot voice conversion (VC) capabilities. Nevertheless, prevailing VC models rooted in language models usually utilize offline conversion from source semantics to acoustic features, necessitating the entirety of the source speech and limiting their application to real-time scenarios.

In this research, a team of researchers from Northwestern Polytechnical University, China, and ByteDance introduce StreamVoice. StreamVoice is a novel streaming language model (LM)-based method for zero-shot voice conversion (VC), allowing real-time conversion with any speaker prompts and source speech. StreamVoice achieves streaming capability by employing a fully causal context-aware LM with a temporal-independent acoustic predictor.  

This model alternately processes semantic and acoustic features at each autoregression time step, eliminating the need for complete source speech. To mitigate potential performance degradation in streaming processing due to incomplete context, two strategies are employed: 

1) teacher-guided context foresight, where a teacher model summarises present and future semantic context during training to guide the model’s forecasting for missing context.

2) semantic masking strategy, promoting acoustic prediction from preceding corrupted semantic and acoustic input to enhance context-learning ability. Notably, StreamVoice stands out as the first LM-based streaming zero-shot VC model without any future look-ahead. Experimental results showcase StreamVoice’s streaming conversion capability while maintaining zero-shot performance comparable to non-streaming VC systems.

The above figure demonstrates the concept of the streaming zero-shot VC employing the widely used recognition-synthesis framework. StreamVoice is built on this popular paradigm. The experiments conducted illustrate that StreamVoice exhibits the capability to conduct speech conversion in a streaming fashion, achieving high speaker similarity for both familiar and unfamiliar speakers. It maintains performance levels comparable to non-streaming voice conversion (VC) systems. As the initial language model (LM)-based zero-shot VC model without any future lookahead, StreamVoice’s entire pipeline incurs only 124 ms latency for the conversion process. This is notably 2.4 times faster than real-time on a single A100 GPU, even without engineering optimizations. The team’s future work involves using more training data to improve StreamVoice’s modeling ability. They also plan to optimize the streaming pipeline, incorporating a high-fidelity codec with a low bitrate and a unified streaming model.

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The post This AI Paper from China Introduces StreamVoice: A Novel Language Model-Based Zero-Shot Voice Conversion System Designed for Streaming Scenarios appeared first on MarkTechPost.

In language model alignment, the effectiveness of reinforcement learning from human feedback (RLHF) hinges on the excellence of the underlying reward model. A pivotal concern is ensuring the high quality of this reward model, as it significantly influences the success of RLHF applications. The challenge lies in developing a reward model that accurately reflects human preferences, a critical factor in achieving optimal performance and alignment in language models.

Recent advancements in large language models (LLMs) have been facilitated by aligning their behavior with human values. RLHF, a prevalent strategy, guides models toward preferred outputs by defining a nuanced loss function reflecting subjective text quality. However, accurately modeling human preferences involves costly data collection. The quality of preference models depends on feedback quantity, response distribution, and label accuracy. 

The researchers from ETH Zurich, Max Planck Institute for Intelligent Systems, Tubingen, and Google Research have introduced West-of-N: Synthetic Preference Generation for Improved Reward Modeling, a novel method to enhance reward model quality by incorporating synthetic preference data into the training dataset. Building on the success of Best-of-N sampling strategies in language model training, they extend this approach to reward model training. The proposed self-training strategy generates preference pairs by selecting the best and worst candidates from response pools to specific queries. 

The proposed West-of-N method generates synthetic preference data by selecting the best and worst responses to a given query from the language model’s policy. Inspired by Best-of-N sampling strategies, this self-training strategy significantly enhances reward model performance, comparable to the impact of incorporating a similar quantity of human preference data. The approach is detailed in Algorithm 1, which includes a theoretical guarantee of correct labeling for generated preference pairs. Filtering steps based on model confidence and response distribution further enhance the quality of the generated data.

The study evaluates the West-of-N synthetic preference data generation method on the Reddit TL;DR summarization and Anthropic Helpful and Harmless dialogue datasets. Results indicate that West-of-N significantly enhances reward model performance, surpassing gains from additional human feedback data and outperforming other synthetic preference generation methods such as RLAIF and RLCD. West-of-N consistently improves model accuracy, Best-of-N sampling, and RL-finetuning across different base preference types, demonstrating its effectiveness in language model alignment.

To conclude, The researchers from Google Research and other institutions have proposed an effective strategy, West-of-N, to enhance reward model (RM) performance in RLHF. Experimental results showcase the method’s efficacy across diverse initial preference data and datasets. The study highlights the potential of Best-of-N sampling and semi-supervised learning for preference modeling. They further suggested further exploring methods like noisy student training to elevate RM performance in conjunction with West-of-N.

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The post This AI Paper from ETH Zurich, Google, and Max Plank Proposes an Effective AI Strategy to Boost the Performance of Reward Models for RLHF (Reinforcement Learning from Human Feedback) appeared first on MarkTechPost.

Language models (LMs), such as GPT-4, are at the forefront of natural language processing, offering capabilities that range from crafting complex prose to solving intricate computational problems. Despite their advanced functionalities, these models need fixing, sometimes yielding inaccurate or conflicting outputs. The challenge lies in enhancing their precision and versatility, particularly in complex, multi-faceted tasks.

A key issue with current language models is their occasional inaccuracy and limitation in handling diverse and complex tasks. While these models excel in many areas, their efficacy could improve when confronted with tasks that demand nuanced understanding or specialized knowledge beyond their general capabilities.

Traditionally, the enhancement of language models has relied on various scaffolding techniques. These methods typically necessitate specific, task-oriented instructions and often need to be revised for tasks requiring dynamic and heuristic approaches or iterative problem-solving. Closing this gap is key to advancing AI and language processing. With it, systems can communicate with humans. We must find solutions to unlock their full potential.

Enter the concept of ‘meta-prompting,’ a groundbreaking technique developed by researchers from Stanford University and OpenAI that elevates the functionality of language models like GPT-4. This approach involves the LM as a multi-dimensional entity that dissects complex tasks into smaller, manageable components. Each component is then delegated to specialized ‘expert’ models within the same overarching LM framework. These experts, guided by detailed and specific instructions, work in concert to address different facets of the task.

Meta-prompting transforms a single LM into a conductor orchestrating a symphony of expert models. It harnesses these models’ specialized knowledge, allowing them to tackle the task at hand collectively. This method enables the LM to maintain a coherent line of reasoning and approach while tapping into a diverse array of expert roles, thereby producing more accurate, reliable, and consistent responses.

Meta-prompting’s performance, particularly when augmented with a Python interpreter, marks a significant advancement in the field. This technique has been shown to outperform standard prompting methods across various tasks, demonstrating its superior flexibility and effectiveness. Integrating a Python interpreter further broadens the applicability of meta-prompting, enabling the LM to handle a wider range of tasks more efficiently.

Through rigorous experimentation with GPT-4, the research team demonstrated the superiority of meta-prompting over traditional scaffolding methods. The empirical results revealed notable improvements in task accuracy and robustness, illustrating the method’s potential for broad application beyond purely computational problems. Meta-prompting’s ability to adapt to different tasks while maintaining high levels of accuracy and coherence makes it a promising direction for future developments in language processing technology.

The research presents meta-prompting as a significant enhancement to language models’ functionality. It effectively addresses complex tasks by intelligently distributing them among specialized experts within the same model. This innovative approach augments the model’s problem-solving capabilities and opens up new possibilities for advancements in artificial intelligence and natural language processing.

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The post Researchers from Stanford and OpenAI Introduce ‘Meta-Prompting’: An Effective Scaffolding Technique Designed to Enhance the Functionality of Language Models in a Task-Agnostic Manner appeared first on MarkTechPost.

Developing foundation models like Large Language Models (LLMs), Vision Transformers (ViTs), and multimodal models marks a significant milestone. These models, known for their versatility and adaptability, are reshaping the approach towards AI applications. However, the growth of these models is accompanied by a considerable increase in resource demands, making their development and deployment a resource-intensive task.

The primary challenge in deploying these foundation models is their substantial resource requirements. The training and maintenance of models such as LLaMa-270B involve immense computational power and energy, leading to high costs and significant environmental impacts. This resource-intensive nature limits their accessibility, confining the ability to train and deploy these models to entities with substantial computational resources.

In response to the challenges of resource efficiency, significant research efforts are directed toward developing more resource-efficient strategies. These efforts encompass algorithm optimization, system-level innovations, and novel architecture designs. The goal is to minimize the resource footprint without compromising the models’ performance and capabilities. This includes exploring various techniques to optimize algorithmic efficiency, enhance data management, and innovate system architectures to reduce the computational load.

The survey by researchers from Beijing University of Posts and Telecommunications, Peking University, and Tsinghua University delves into the evolution of language foundation models, detailing their architectural developments and the downstream tasks they perform. It highlights the transformative impact of the Transformer architecture, attention mechanisms, and the encoder-decoder structure in language models. The survey also sheds light on speech foundation models, which can derive meaningful representations from raw audio signals, and their computational costs.

Vision foundation models are another focus area. Encoder-only architectures like ViT, DeiT, and SegFormer have significantly advanced the field of computer vision, demonstrating impressive results in image classification and segmentation. Despite their resource demands, these models have pushed the boundaries of self-supervised pre-training in vision models.

A growing area of interest is multimodal foundation models, which aim to encode data from different modalities into a unified latent space. These models typically employ transformer encoders for data encoding or decoders for cross-modal generation. The survey discusses key architectures, such as multi-encoder and encoder-decoder models, representative models in cross-modal generation, and their cost analysis.

The document offers an in-depth look into the current state and future directions of resource-efficient algorithms and systems in foundation models. It provides valuable insights into various strategies employed to address the issues posed by these models’ large resource footprint. The document underscores the importance of continued innovation to make foundation models more accessible and sustainable.

Key takeaways from the survey include:

Increased resource demands mark the evolution of foundation models.

Innovative strategies are being developed to enhance the efficiency of these models.

The goal is to minimize the resource footprint while maintaining performance.

Efforts span across algorithm optimization, data management, and system architecture innovation.

The document highlights the impact of these models in language, speech, and vision domains.

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With the advancement of AI in recent times, large language models are being used in many fields. These models are trained on larger datasets and require bigger training datasets. These are used in various natural language processing (NLP) tasks, such as dialogue systems, machine translation, information retrieval, etc. There has been thorough research in LLMs to formulate new useful models in NLP.

Recently, researchers from Orion Star have come up with a new framework, Orion-14B. This Orion-14B-Base model is trained on 14 billion parameters, and the base model is trained on a huge 2.5 trillion tokens and spans from languages such as Chinese, English, Japanese, and Korean. Also, this framework has an impressive 200,000-token context length. The Orion-14B series comprises several models with specific, unique features and applications. 

The Orion-14B includes models appropriate for specific tasks. One is Orion-14B-Chat-RAG, fine-tuned on a custom retrieval augmented generation dataset, so Orion-14B-Chat-RAG performs well in retrieval increased generation tasks. Orion-14B also has Orion-14B-Chat-Plugin, among other models, designed for agent-related scenarios. In this, the LLM acts as a plugin and function call system. Also, the framework has several other extensions to Orion-14B, involving a long context model, a quantized model, and several other application-oriented models.

The research team emphasized that Orion-14B series models are adaptable and excel in human-annotated blind tests. Its long-chat version can handle lengthy texts and support up to 320,000 tokens. Also, the Orion-14B’s quantized versions have enhanced the efficiency; therefore, the model size was reduced by 70%. It also improved inference speed by 30%, with a minimal performance loss of less than 1%. Further, the researchers emphasized that this model has significantly reduced the model size while increasing inference speed and has only a marginal 1% performance loss. Additionally, they highlighted that this model can perform better than other models of the 20-billion parameter scale level as it excels in comprehensive evaluations and displays robust multilingual capabilities, particularly outperforming in Japanese and Korean test sets.

The dataset used for these models has multilingual texts, focusing on English and Chinese, which account for 90% of the entire dataset. They are also trying to include Japanese and Korean texts in more than 5% of the content. The remaining portion of the dataset has texts in various languages, such as Spanish, French, German, Arabic, and more. This dataset has written language across many topics, including web pages, news articles, encyclopedic entries, books, source code, and academic publications.

The research team emphasized that they faced many obstacles in formulating these models. In conclusion, the Orion-14B series is a significant step in multilingual large language models. This series outperforms other open-source models and is a potential strong baseline for future LLM research. The researchers are focusing on enhancing the efficiency of the series of these models, which can strengthen the LLM research in this field.

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Proteins are essential for various cellular functions, providing vital amino acids for humans. Understanding proteins is crucial for human biology and health, requiring advanced machine-learning models for protein representation. Self-supervised pre-training, inspired by natural language processing, has significantly improved protein sequence representation. However, existing models need help handling longer sequences and maintaining contextual understanding. Strategies like linearized and sparse approximations have been used to address computational demands but often compromise expressivity. Despite advancements, models with over 100 million parameters struggle with larger inputs. The role of individual amino acids poses a unique challenge, requiring a nuanced approach for accurate modeling. 

Researchers from the Tokyo Institute of Technology, Japan, have developed  ProtHyena. This rapid and resource-efficient foundation model incorporates the Hyena operator for analyzing protein data. Unlike traditional attention-based methods, ProtHyena is designed to capture both long-range context and single amino acid resolution in real protein sequences. The researchers pretrained the model using the Pfam dataset. They fine-tuned it for various protein-related tasks, achieving performance comparable to or even surpassing state-of-the-art approaches in some cases.

Traditional language models based on the Transformer and BERT architectures demonstrate effectiveness in various applications. Still, they are limited by the quadratic computational complexity of the attention mechanism, which restricts their efficiency and the length of context they can process. Various methods have been developed to address the high computational cost of self-attention for long sequences, such as factorized self-attention used in sparse Transformers and the Performer, which decomposes the self-attention matrix. These methods allow for processing longer sequences but often come with a trade-off in model expressivity. 

ProtHyena is an approach that leverages the Hyena operator to address the limitations of attention mechanisms in traditional language models. ProtHyena uses the natural protein vocabulary, treating each amino acid as an individual token, and incorporates special character tokens for padding, separation, and unknown characters. The Hyena operator is defined by a recurrent structure comprising long convolutions and element-wise gating. The study also compares ProtHyena with a variant model called ProtHyena-bpe, which employs byte pair encoding (BPE) for data compression and utilizes a larger vocabulary size. 

ProtHyena addresses the limitations of traditional models based on the Transformer and BERT architectures. ProtHyena achieved state-of-the-art results in various downstream tasks, including Remote Homology and Fluorescence prediction, outperforming contemporary models like TAPE Transformer and SPRoBERTa. Regarding Remote Homology, ProtHyena reached the highest accuracy of 0.317, surpassing other models that scored 0.210 and 0.230. For Fluorescence prediction, ProtHyena demonstrated robustness with a Spearman’s r of 0.678, showcasing its ability to capture complex protein properties. ProtHyena also showed promising results in Secondary Structure Prediction (SSP) and Stability tasks, although the provided sources did not mention specific metrics.

In conclusion, ProtHyena, a protein language model, integrates the Hyena operator to address the computational challenges faced by attention-based models. ProtHyena efficiently processes long protein sequences and delivers state-of-the-art performance in various downstream tasks, surpassing traditional models with only a fraction of the parameters required. The comprehensive pre-training and fine-tuning of ProtHyena on the expansive Pfam dataset across ten different tasks demonstrate its ability to capture complex biological information accurately and accurately. Adopting the Hyena operator enables ProtHyena to perform at a subquadratic time complexity, offering a significant leap forward in protein sequence analysis.

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In recent times, Large Language Models (LLMs) have gained popularity for their ability to respond to user queries in a more human-like manner, accomplished through reinforcement learning. However, aligning these LLMs with human preferences in reinforcement learning from human feedback (RLHF) can lead to a phenomenon known as reward hacking. This occurs when LLMs exploit flaws in the reward model (RM), achieving high rewards without fulfilling the underlying objectives, as illustrated in Figure 1(b). Reward hacking raises concerns such as degraded performance, checkpoint selection challenges, potential biases, and, most critically, safety risks.

The primary challenges identified in designing RMs to mitigate reward hacking include distribution shifts and inconsistent preferences in the preference dataset. Distribution shifts arise due to policy drift during RL, leading to a deviation from the offline preference dataset. Inconsistent preferences stem from noisy binary labels, introducing low inter-labeler agreement and impacting RM robustness. To address these challenges, existing approaches have explored strategies like KL regularization, active learning, and prediction ensembling (ENS). However, these methods face efficiency issues, reliability concerns, and struggle with preference inconsistencies.

To tackle these challenges, this paper proposes Weight Averaged Reward Models (WARM) (illustrated in Figure 1(a)), a simple, efficient, and scalable strategy for obtaining a reliable and robust RM. WARM combines multiple RMs through linear interpolation in the weight space, providing benefits such as efficiency, improved reliability under distribution shifts, and enhanced robustness to label corruption. The diversity across fine-tuned weights is a key contributor to the effectiveness of WARM.

Reference: https://arxiv.org/pdf/2401.12187.pdf

WARM is compared to prediction ensembling (ENS), showcasing its efficiency and practicality by requiring a single model at inference time, eliminating memory and inference overheads. Empirical results indicate that WARM performs similarly to ENS in terms of variance reduction but exhibits superiority under distribution shifts. The paper introduces the concept of linear mode connectivity (LMC) as a key factor in WARM’s success, demonstrating its ability to memorize less and generalize better than ensembling predictions. There are 3 observations that are made in the experiments and are empirically proven in Figure 3 and 4:

Observation 1 (LMC): The accuracy of the interpolated model is at least as good as the interpolation of the individual accuracies.

Observation 2 (WA and ENS): Weight averaging and prediction ensembling perform similarly.

Observation 3 (WA and ENS): The accuracy gains of WA over ENS grow as data moves away from the training distribution. 

Reference: https://arxiv.org/pdf/2401.12187.pdf

The benefits of WARM extend beyond its primary goals. It aligns with the updatable machine learning paradigm, allowing parallelization in federated learning scenarios. WARM could contribute to privacy and bias mitigation by reducing memorization of private preferences. The method shows potential for combining RMs trained on different datasets, supporting iterative and evolving preferences. Further exploration includes extending WARM to direct preference optimization strategies.

Despite its innovation, WARM has limitations compared to prediction ensembling methods, including potential limitations in handling diverse architectures and uncertainty estimation. WARM does not entirely eliminate spurious correlations or biases in preference data, suggesting the need for additional methods for a comprehensive solution. Lastly, WARM focuses on enhancing reward modeling and should be considered within the broader context of responsible AI to address safety risks from misalignment.

In conclusion, Weight Averaged Reward Models (WARM) offer a promising solution to challenges in reward modeling, enhancing alignment in RLHF. The paper’s empirical results and theoretical insights position WARM as a valuable contribution toward creating more aligned, transparent, and effective AI systems.

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Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. Examples of applications include conversational search, customer support agent assistance, customer support analytics, self-service virtual assistants, chatbots, rich media generation, content moderation, coding companions to accelerate secure, high-performance software development, deeper insights from multimodal content sources, acceleration of your organization’s security investigations and mitigations, and much more. Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring. Being aware of risks fosters transparency and trust in generative AI applications, encourages increased observability, helps to meet compliance requirements, and facilitates informed decision-making by leaders.
The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed. Specifically, this post seeks to help AI/ML and data scientists who may not have had previous exposure to security principles gain an understanding of core security and privacy best practices in the context of developing generative AI applications using LLMs. We also discuss common security concerns that can undermine trust in AI, as identified by the Open Worldwide Application Security Project (OWASP) Top 10 for LLM Applications, and show ways you can use AWS to increase your security posture and confidence while innovating with generative AI.
This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs. We first delve into the vulnerabilities, threats, and risks that arise from the implementation, deployment, and use of LLM solutions, and provide guidance on how to start innovating with security in mind. We then discuss how building on a secure foundation is essential for generative AI. Lastly, we connect these together with an example LLM workload to describe an approach towards architecting with defense-in-depth security across trust boundaries.
By the end of this post, AI/ML engineers, data scientists, and security-minded technologists will be able to identify strategies to architect layered defenses for their generative AI applications, understand how to map OWASP Top 10 for LLMs security concerns to some corresponding controls, and build foundational knowledge towards answering the following top AWS customer question themes for their applications:

What are some of the common security and privacy risks with using generative AI based on LLMs in my applications that I can most impact with this guidance?
What are some ways to implement security and privacy controls in the development lifecycle for generative AI LLM applications on AWS?
What operational and technical best practices can I integrate into how my organization builds generative AI LLM applications to manage risk and increase confidence in generative AI applications using LLMs?

Improve security outcomes while developing generative AI
Innovation with generative AI using LLMs requires starting with security in mind to develop organizational resiliency, build on a secure foundation, and integrate security with a defense in depth security approach. Security is a shared responsibility between AWS and AWS customers. All the principles of the AWS Shared Responsibility Model are applicable to generative AI solutions. Refresh your understanding of the AWS Shared Responsibility Model as it applies to infrastructure, services, and data when you build LLM solutions.
Start with security in mind to develop organizational resiliency
Start with security in mind to develop organizational resiliency for developing generative AI applications that meet your security and compliance objectives. Organizational resiliency draws on and extends the definition of resiliency in the AWS Well-Architected Framework to include and prepare for the ability of an organization to recover from disruptions. Consider your security posture, governance, and operational excellence when assessing overall readiness to develop generative AI with LLMs and your organizational resiliency to any potential impacts. As your organization advances its use of emerging technologies such as generative AI and LLMs, overall organizational resiliency should be considered as a cornerstone of a layered defensive strategy to protect assets and lines of business from unintended consequences.
Organizational resiliency matters substantially for LLM applications
Although all risk management programs can benefit from resilience, organizational resiliency matters substantially for generative AI. Five of the OWASP-identified top 10 risks for LLM applications rely on defining architectural and operational controls and enforcing them at an organizational scale in order to manage risk. These five risks are insecure output handling, supply chain vulnerabilities, sensitive information disclosure, excessive agency, and overreliance. Begin increasing organizational resiliency by socializing your teams to consider AI, ML, and generative AI security a core business requirement and top priority throughout the whole lifecycle of the product, from inception of the idea, to research, to the application’s development, deployment, and use. In addition to awareness, your teams should take action to account for generative AI in governance, assurance, and compliance validation practices.
Build organizational resiliency around generative AI
Organizations can start adopting ways to build their capacity and capabilities for AI/ML and generative AI security within their organizations. You should begin by extending your existing security, assurance, compliance, and development programs to account for generative AI.
The following are the five key areas of interest for organizational AI, ML, and generative AI security:

Understand the AI/ML security landscape
Include diverse perspectives in security strategies
Take action proactively for securing research and development activities
Align incentives with organizational outcomes
Prepare for realistic security scenarios in AI/ML and generative AI

Develop a threat model throughout your generative AI Lifecycle
Organizations building with generative AI should focus on risk management, not risk elimination, and include threat modeling in and business continuity planning the planning, development, and operations of generative AI workloads. Work backward from production use of generative AI by developing a threat model for each application using traditional security risks as well as generative AI-specific risks. Some risks may be acceptable to your business, and a threat modeling exercise can help your company identify what your acceptable risk appetite is. For example, your business may not require 99.999% uptime on a generative AI application, so the additional recovery time associated to recovery using AWS Backup with Amazon S3 Glacier may be an acceptable risk. Conversely, the data in your model may be extremely sensitive and highly regulated, so deviation from AWS Key Management Service (AWS KMS) customer managed key (CMK) rotation and use of AWS Network Firewall to help enforce Transport Layer Security (TLS) for ingress and egress traffic to protect against data exfiltration may be an unacceptable risk.
Evaluate the risks (inherent vs. residual) of using the generative AI application in a production setting to identify the right foundational and application-level controls. Plan for rollback and recovery from production security events and service disruptions such as prompt injection, training data poisoning, model denial of service, and model theft early on, and define the mitigations you will use as you define application requirements. Learning about the risks and controls that need to be put in place will help define the best implementation approach for building a generative AI application, and provide stakeholders and decision-makers with information to make informed business decisions about risk. If you are unfamiliar with the overall AI and ML workflow, start by reviewing 7 ways to improve security of your machine learning workloads to increase familiarity with the security controls needed for traditional AI/ML systems.
Just like building any ML application, building a generative AI application involves going through a set of research and development lifecycle stages. You may want to review the AWS Generative AI Security Scoping Matrix to help build a mental model to understand the key security disciplines that you should consider depending on which generative AI solution you select.
Generative AI applications using LLMs are typically developed and operated following ordered steps:

Application requirements – Identify use case business objectives, requirements, and success criteria
Model selection – Select a foundation model that aligns with use case requirements
Model adaptation and fine-tuning – Prepare data, engineer prompts, and fine-tune the model
Model evaluation – Evaluate foundation models with use case-specific metrics and select the best-performing model
Deployment and integration – Deploy the selected foundation model on your optimized infrastructure and integrate with your generative AI application
Application monitoring – Monitor application and model performance to enable root cause analysis

Ensure teams understand the critical nature of security as part of the design and architecture phases of your software development lifecycle on Day 1. This means discussing security at each layer of your stack and lifecycle, and positioning security and privacy as enablers to achieving business objectives.Architect controls for threats before you launch your LLM application, and consider whether the data and information you will use for model adaptation and fine-tuning warrants controls implementation in the research, development, and training environments. As part of quality assurance tests, introduce synthetic security threats (such as attempting to poison training data, or attempting to extract sensitive data through malicious prompt engineering) to test out your defenses and security posture on a regular basis.
Additionally, stakeholders should establish a consistent review cadence for production AI, ML, and generative AI workloads and set organizational priority on understanding trade-offs between human and machine control and error prior to launch. Validating and assuring that these trade-offs are respected in the deployed LLM applications will increase the likelihood of risk mitigation success.
Build generative AI applications on secure cloud foundations
At AWS, security is our top priority. AWS is architected to be the most secure global cloud infrastructure on which to build, migrate, and manage applications and workloads. This is backed by our deep set of over 300 cloud security tools and the trust of our millions of customers, including the most security-sensitive organizations like government, healthcare, and financial services. When building generative AI applications using LLMs on AWS, you gain security benefits from the secure, reliable, and flexible AWS Cloud computing environment.
Use an AWS global infrastructure for security, privacy, and compliance
When you develop data-intensive applications on AWS, you can benefit from an AWS global Region infrastructure, architected to provide capabilities to meet your core security and compliance requirements. This is reinforced by our AWS Digital Sovereignty Pledge, our commitment to offering you the most advanced set of sovereignty controls and features available in the cloud. We are committed to expanding our capabilities to allow you to meet your digital sovereignty needs, without compromising on the performance, innovation, security, or scale of the AWS Cloud. To simplify implementation of security and privacy best practices, consider using reference designs and infrastructure as code resources such as the AWS Security Reference Architecture (AWS SRA) and the AWS Privacy Reference Architecture (AWS PRA). Read more about architecting privacy solutions, sovereignty by design, and compliance on AWS and use services such as AWS Config, AWS Artifact, and AWS Audit Manager to support your privacy, compliance, audit, and observability needs.
Understand your security posture using AWS Well-Architected and Cloud Adoption Frameworks
AWS offers best practice guidance developed from years of experience supporting customers in architecting their cloud environments with the AWS Well-Architected Framework and in evolving to realize business value from cloud technologies with the AWS Cloud Adoption Framework (AWS CAF). Understand the security posture of your AI, ML, and generative AI workloads by performing a Well-Architected Framework review. Reviews can be performed using tools like the AWS Well-Architected Tool, or with the help of your AWS team through AWS Enterprise Support. The AWS Well-Architected Tool automatically integrates insights from AWS Trusted Advisor to evaluate what best practices are in place and what opportunities exist to improve functionality and cost-optimization. The AWS Well-Architected Tool also offers customized lenses with specific best practices such as the Machine Learning Lens for you to regularly measure your architectures against best practices and identify areas for improvement. Checkpoint your journey on the path to value realization and cloud maturity by understanding how AWS customers adopt strategies to develop organizational capabilities in the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI. You might also find benefit in understanding your overall cloud readiness by participating in an AWS Cloud Readiness Assessment. AWS offers additional opportunities for engagement—ask your AWS account team for more information on how to get started with the Generative AI Innovation Center.
Accelerate your security and AI/ML learning with best practices guidance, training, and certification
AWS also curates recommendations from Best Practices for Security, Identity, & Compliance and AWS Security Documentation to help you identify ways to secure your training, development, testing, and operational environments. If you’re just getting started, dive deeper on security training and certification, consider starting with AWS Security Fundamentals and the AWS Security Learning Plan. You can also use the AWS Security Maturity Model to help guide you finding and prioritizing the best activities at different phases of maturity on AWS, starting with quick wins, through foundational, efficient, and optimized stages. After you and your teams have a basic understanding of security on AWS, we strongly recommend reviewing How to approach threat modeling and then leading a threat modeling exercise with your teams starting with the Threat Modeling For Builders Workshop training program. There are many other AWS Security training and certification resources available.
Apply a defense-in-depth approach to secure LLM applications
Applying a defense-in-depth security approach to your generative AI workloads, data, and information can help create the best conditions to achieve your business objectives. Defense-in-depth security best practices mitigate many of the common risks that any workload faces, helping you and your teams accelerate your generative AI innovation. A defense-in-depth security strategy uses multiple redundant defenses to protect your AWS accounts, workloads, data, and assets. It helps make sure that if any one security control is compromised or fails, additional layers exist to help isolate threats and prevent, detect, respond, and recover from security events. You can use a combination of strategies, including AWS services and solutions, at each layer to improve the security and resiliency of your generative AI workloads.

Many AWS customers align to industry standard frameworks, such as the NIST Cybersecurity Framework. This framework helps ensure that your security defenses have protection across the pillars of Identify, Protect, Detect, Respond, Recover, and most recently added, Govern. This framework can then easily map to AWS Security services and those from integrated third parties as well to help you validate adequate coverage and policies for any security event your organization encounters.

Defense in depth: Secure your environment, then add enhanced AI/ML-specific security and privacy capabilities
A defense-in-depth strategy should start by protecting your accounts and organization first, and then layer on the additional built-in security and privacy enhanced features of services such as Amazon Bedrock and Amazon SageMaker. Amazon has over 30 services in the Security, Identity, and Compliance portfolio which are integrated with AWS AI/ML services, and can be used together to help secure your workloads, accounts, organization. To properly defend against the OWASP Top 10 for LLM, these should be used together with the AWS AI/ML services.
Start by implementing a policy of least privilege, using services like IAM Access Analyzer to look for overly permissive accounts, roles, and resources to restrict access using short-termed credentials. Next, make sure that all data at rest is encrypted with AWS KMS, including considering the use of CMKs, and all data and models are versioned and backed up using Amazon Simple Storage Service (Amazon S3) versioning and applying object-level immutability with Amazon S3 Object Lock. Protect all data in transit between services using AWS Certificate Manager and/or AWS Private CA, and keep it within VPCs using AWS PrivateLink. Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies. Consider inserting AWS Web Application Firewall (AWS WAF) in front to protect web applications and APIs from malicious bots, SQL injection attacks, cross-site scripting (XSS), and account takeovers with Fraud Control. Logging with AWS CloudTrail, Amazon Virtual Private Cloud (Amazon VPC) flow logs, and Amazon Elastic Kubernetes Service (Amazon EKS) audit logs will help provide forensic review of each transaction available to services such as Amazon Detective. You can use Amazon Inspector to automate vulnerability discovery and management for Amazon Elastic Compute Cloud (Amazon EC2) instances, containers, AWS Lambda functions, and identify the network reachability of your workloads. Protect your data and models from suspicious activity using Amazon GuardDuty’s ML-powered threat models and intelligence feeds, and enabling its additional features for EKS Protection, ECS Protection, S3 Protection, RDS Protection, Malware Protection, Lambda Protection, and more. You can use services like AWS Security Hub to centralize and automate your security checks to detect deviations from security best practices and accelerate investigation and automate remediation of security findings with playbooks. You can also consider implementing a zero trust architecture on AWS to further increase fine-grained authentication and authorization controls for what human users or machine-to-machine processes can access on a per-request basis. Also consider using Amazon Security Lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in your account. With Security Lake, you can get a more complete understanding of your security data across your entire organization.
After your generative AI workload environment has been secured, you can layer in AI/ML-specific features, such as Amazon SageMaker Data Wrangler to identify potential bias during data preparation and Amazon SageMaker Clarify to detect bias in ML data and models. You can also use Amazon SageMaker Model Monitor to evaluate the quality of SageMaker ML models in production, and notify you when there is drift in data quality, model quality, and feature attribution. These AWS AI/ML services working together (including SageMaker working with Amazon Bedrock) with AWS Security services can help you identify potential sources of natural bias and protect against malicious data tampering. Repeat this process for each of the OWASP Top 10 for LLM vulnerabilities to ensure you’re maximizing the value of AWS services to implement defense in depth to protect your data and workloads.
As AWS Enterprise Strategist Clarke Rodgers wrote in his blog post “CISO Insight: Every AWS Service Is A Security Service”, “I would argue that virtually every service within the AWS cloud either enables a security outcome by itself, or can be used (alone or in conjunction with one or more services) by customers to achieve a security, risk, or compliance objective.” And “Customer Chief Information Security Officers (CISOs) (or their respective teams) may want to take the time to ensure that they are well versed with all AWS services because there may be a security, risk, or compliance objective that can be met, even if a service doesn’t fall into the ‘Security, Identity, and Compliance’ category.”
Layer defenses at trust boundaries in LLM applications
When developing generative AI-based systems and applications, you should consider the same concerns as with any other ML application, as mentioned in the MITRE ATLAS Machine Learning Threat Matrix, such as being mindful of software and data component origins (such as performing an open source software audit, reviewing software bill of materials (SBOMs), and analyzing data workflows and API integrations) and implementing necessary protections against LLM supply chain threats. Include insights from industry frameworks, and be aware of ways to use multiple sources of threat intelligence and risk information to adjust and extend your security defenses to account for AI, ML, and generative AI security risks that are emergent and not included in traditional frameworks. Seek out companion information on AI-specific risks from industry, defense, governmental, international, and academic sources, because new threats emerge and evolve in this space regularly and companion frameworks and guides are updated frequently. For example, when using a Retrieval Augmented Generation (RAG) model, if the model doesn’t include the data it needs, it may request it from an external data source for using during inferencing and fine-tuning. The source that it queries may be outside of your control, and can be a potential source of compromise in your supply chain. A defense-in-depth approach should be extended towards external sources to establish trust, authentication, authorization, access, security, privacy, and accuracy of the data it is accessing. To dive deeper, read “Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart”
Analyze and mitigate risk in your LLM applications
In this section, we analyze and discuss some risk mitigation techniques based on trust boundaries and interactions, or distinct areas of the workload with similar appropriate controls scope and risk profile. In this sample architecture of a chatbot application, there are five trust boundaries where controls are demonstrated, based on how AWS customers commonly build their LLM applications. Your LLM application may have more or fewer definable trust boundaries. In the following sample architecture, these trust boundaries are defined as:

User interface interactions (request and response)
Application interactions
Model interactions
Data interactions
Organizational interactions and use

User interface interactions: Develop request and response monitoring
Detect and respond to cyber incidents related to generative AI in a timely manner by evaluating a strategy to address risk from the inputs and outputs of the generative AI application. For example, additional monitoring for behaviors and data outflow may need to be instrumented to detect sensitive information disclosure outside your domain or organization, in the case that it is used in the LLM application.
Generative AI applications should still uphold the standard security best practices when it comes to protecting data. Establish a secure data perimeter and secure sensitive data stores. Encrypt data and information used for LLM applications at rest and in transit. Protect data used to train your model from training data poisoning by understanding and controlling which users, processes, and roles are allowed to contribute to the data stores, as well as how data flows in the application, monitor for bias deviations, and using versioning and immutable storage in storage services such as Amazon S3. Establish strict data ingress and egress controls using services like AWS Network Firewall and AWS VPCs to protect against suspicious input and the potential for data exfiltration.
During the training, retraining, or fine-tuning process, you should be aware of any sensitive data that is utilized. After data is used during one of these processes, you should plan for a scenario where any user of your model suddenly becomes able to extract the data or information back out by utilizing prompt injection techniques. Understand the risks and benefits of using sensitive data in your models and inferencing. Implement robust authentication and authorization mechanisms for establishing and managing fine-grained access permissions, which don’t rely on LLM application logic to prevent disclosure. User-controlled input to a generative AI application has been demonstrated under some conditions to be able to provide a vector to extract information from the model or any non-user-controlled parts of the input. This can occur via prompt injection, where the user provides input that causes the output of the model to deviate from the expected guardrails of the LLM application, including providing clues to the datasets that the model was originally trained on.
Implement user-level access quotas for users providing input and receiving output from a model. You should consider approaches that don’t allow anonymous access under conditions where the model training data and information is sensitive, or where there is risk from an adversary training a facsimile of your model based on their input and your aligned model output. In general, if part of the input to a model consists of arbitrary user-provided text, consider the output to be susceptible to prompt injection, and accordingly ensure use of the outputs includes implemented technical and organizational countermeasures to mitigate insecure output handling, excessive agency, and overreliance. In the example earlier related to filtering for malicious input using AWS WAF, consider building a filter in front of your application for such potential misuse of prompts, and develop a policy for how to handle and evolve those as your model and data grows. Also consider a filtered review of the output before it is returned to the user to ensure it meets quality, accuracy, or content moderation standards. You may want to further customize this for your organization’s needs with an additional layer of control on inputs and outputs in front of your models to mitigate suspicious traffic patterns.
Application interactions: Application security and observability
Review your LLM application with attention to how a user could utilize your model to bypass standard authorization to a downstream tool or toolchain that they don’t have authorization to access or use. Another concern at this layer involves accessing external data stores by using a model as an attack mechanism using unmitigated technical or organizational LLM risks. For example, if your model is trained to access certain data stores that could contain sensitive data, you should ensure that you have proper authorization checks between your model and the data stores. Use immutable attributes about users that don’t come from the model when performing authorization checks. Unmitigated insecure output handling, insecure plugin design, and excessive agency can create conditions where a threat actor may use a model to trick the authorization system into escalating effective privileges, leading to a downstream component believing the user is authorized to retrieve data or take a specific action.
When implementing any generative AI plugin or tool, it is imperative to examine and comprehend the level of access being granted, as well as scrutinize the access controls that have been configured. Using unmitigated insecure generative AI plugins may render your system susceptible to supply chain vulnerabilities and threats, potentially leading to malicious actions, including running remote code.
Model interactions: Model attack prevention
You should be aware of the origin of any models, plugins, tools, or data you use, in order to evaluate and mitigate against supply chain vulnerabilities. For example, some common model formats permit the embedding of arbitrary runnable code in the models themselves. Use package mirrors, scanning, and additional inspections as relevant to your organizations security goals.
The datasets you train and fine-tune your models on must also be reviewed. If you further automatically fine-tune a model based on user feedback (or other end-user-controllable information), you must consider if a malicious threat actor could change the model arbitrarily based on manipulating their responses and achieve training data poisoning.
Data interactions: Monitor data quality and usage
Generative AI models such as LLMs generally work well because they have been trained on a large amount of data. Although this data helps LLMs complete complex tasks, it also can expose your system to risk of training data poisoning, which occurs when inappropriate data is included or omitted inside a training dataset that can alter a model’s behavior. To mitigate this risk, you should look at your supply chain and understand the data review process for your system before it’s used inside your model. Although the training pipeline is a prime source for data poisoning, you should also look at how your model gets data, such as in a RAG model or data lake, and if the source of that data is trusted and protected. Use AWS Security services such as AWS Security Hub, Amazon GuardDuty, and Amazon Inspector to help continuously monitor for suspicious activity in Amazon EC2, Amazon EKS, Amazon S3, Amazon Relational Database Service (Amazon RDS), and network access that may be indicators of emerging threats, and use Detective to visualize security investigations. Also consider using services such as Amazon Security Lake to accelerate security investigations by creating a purpose-built data lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources which contribute to your AI/ML workloads.
Organizational interactions: Implement enterprise governance guardrails for generative AI
Identify risks associated with the use of generative AI for your businesses. You should build your organization’s risk taxonomy and conduct risk assessments to make informed decisions when deploying generative AI solutions. Develop a business continuity plan (BCP) that includes AI, ML, and generative AI workloads and that can be enacted quickly to replace the lost functionality of an impacted or offline LLM application to meet your SLAs.
Identify process and resource gaps, inefficiencies, and inconsistencies, and improve awareness and ownership across your business. Threat model all generative AI workloads to identify and mitigate potential security threats that may lead to business-impacting outcomes, including unauthorized access to data, denial of service, and resource misuse. Take advantage of the new AWS Threat Composer Modeling Tool to help reduce time-to-value when performing threat modeling. Later in your development cycles, consider including introducing security chaos engineering fault injection experiments to create real-world conditions to understand how your system will react to unknowns and build confidence in the system’s resiliency and security.
Include diverse perspectives in developing security strategies and risk management mechanisms to ensure adherence and coverage for AI/ML and generative security across all job roles and functions. Bring a security mindset to the table from the inception and research of any generative AI application to align on requirements. If you need extra assistance from AWS, ask your AWS account manager to make sure that there is equal support by requesting AWS Solutions Architects from AWS Security and AI/ML to help in tandem.
Ensure that your security organization routinely takes actions to foster communication around both risk awareness and risk management understanding among generative AI stakeholders such as product managers, software developers, data scientists, and executive leadership, allowing threat intelligence and controls guidance to reach the teams that may be impacted. Security organizations can support a culture of responsible disclosure and iterative improvement by participating in discussions and bringing new ideas and information to generative AI stakeholders that relate to their business objectives. Learn more about our commitment to Responsible AI and additional responsible AI resources to help our customers.
Gain advantage in enabling better organizational posture for generative AI by unblocking time to value in the existing security processes of your organization. Proactively evaluate where your organization may require processes that are overly burdensome given the generative AI security context and refine these to provide developers and scientists a clear path to launch with the correct controls in place.
Assess where there may be opportunities to align incentives, derisk, and provide a clear line of sight on the desired outcomes. Update controls guidance and defenses to meet the evolving needs of AI/ML and generative AI application development to reduce confusion and uncertainty that can cost development time, increase risk, and increase impact.
Ensure that stakeholders who are not security experts are able to both understand how organizational governance, policies, and risk management steps apply to their workloads, as well as apply risk management mechanisms. Prepare your organization to respond to realistic events and scenarios that may occur with generative AI applications, and ensure that generative AI builder roles and response teams are aware of escalation paths and actions in case of concern for any suspicious activity.
Conclusion
To successfully commercialize innovation with any new and emerging technology requires starting with a security-first mindset, building on a secure infrastructure foundation, and thinking about how to further integrate security at each level of the technology stack early with a defense-in-depth security approach. This includes interactions at multiple layers of your technology stack, and integration points within your digital supply chain, to ensure organizational resiliency. Although generative AI introduces some new security and privacy challenges, if you follow fundamental security best practices such as using defense-in-depth with layered security services, you can help protect your organization from many common issues and evolving threats. You should implement layered AWS Security services across your generative AI workloads and larger organization, and focus on integration points in your digital supply chains to secure your cloud environments. Then you can use the enhanced security and privacy capabilities in AWS AI/ML services such as Amazon SageMaker and Amazon Bedrock to add further layers of enhanced security and privacy controls to your generative AI applications. Embedding security from the start will make it faster, easier, and more cost-effective to innovate with generative AI, while simplifying compliance. This will help you increase controls, confidence, and observability to your generative AI applications for your employees, customers, partners, regulators, and other concerned stakeholders.
Additional references

Industry standard frameworks for AI/ML-specific risk management and security:

NIST AI Risk Management Framework (AI RMF)
OWASP Top 10 for Large Language Model LLM Applications
MITRE ATLAS Machine Learning Threat Matrix
OWASP AI Security & Privacy Guide

About the authors
Christopher Rae is a Principal Worldwide Security GTM Specialist focused on developing and executing strategic initiatives that accelerate and scale adoption of AWS security services. He is passionate about the intersection of cybersecurity and emerging technologies, with 20+ years of experience in global strategic leadership roles delivering security solutions to media, entertainment, and telecom customers. He recharges through reading, traveling, food and wine, discovering new music, and advising early-stage startups.
Elijah Winter is a Senior Security Engineer in Amazon Security, holding a BS in Cyber Security Engineering and infused with a love for Harry Potter. Elijah excels in identifying and addressing vulnerabilities in AI systems, blending technical expertise with a touch of wizardry. Elijah designs tailored security protocols for AI ecosystems, bringing a magical flair to digital defenses. Integrity driven, Elijah has a security background in both public and commercial sector organizations focused on protecting trust.
Ram Vittal is a Principal ML Solutions Architect at AWS. He has over 3 decades of experience architecting and building distributed, hybrid, and cloud applications. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. In his spare time, he rides his motorcycle and walks with his 3-year-old Sheepadoodle!
Navneet Tuteja is a Data Specialist at Amazon Web Services. Before joining AWS, Navneet worked as a facilitator for organizations seeking to modernize their data architectures and implement comprehensive AI/ML solutions. She holds an engineering degree from Thapar University, as well as a master’s degree in statistics from Texas A&M University.
Emily Soward is a Data Scientist with AWS Professional Services. She holds a Master of Science with Distinction in Artificial Intelligence from the University of Edinburgh in Scotland, United Kingdom with emphasis on Natural Language Processing (NLP). Emily has served in applied scientific and engineering roles focused on AI-enabled product research and development, operational excellence, and governance for AI workloads running at organizations in the public and private sector. She contributes to customer guidance as an AWS Senior Speaker and recently, as an author for AWS Well-Architected in the Machine Learning Lens.

B2B marketing tools have made it possible to scale what only recently consisted of face-to-face meetings with business decision-makers. And it’s technology like marketing automation and machine learning that’s made this possible.

How many digital marketers out there have gasped upon your first overview of the budget allocated to conferences and travel? I certainly have, multiple times.

It’s no secret, with a solid strategy in place, the ROI of B2B digital marketing is significantly better in the large majority of cases. And it’s software that’s driving this dramatic shift.

So, what are the best B2B marketing tools to add to your arsenal right now? We’ve broken our 15 favorite tools into the following categories:

Lead Generation Tools

Sales Enablement Tools

Account-Based Marketing (ABM) Software

Event & Webinar Software

Conversion Rate Optimization (CRO) Software

Social Media Automation Software

B2B Content Marketing Software

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Lead Generation Tools

The following B2B marketing tools have everything required to convert more prospects into customers. You’ll find everything you need from lead generation to lead enrichment software.

#1. B2B Marketing Tool: Website Visitor ID X-Ray Tool

Website Visitor ID X-Ray pixel allows you to identify anonymous visitors on your website, capturing names, emails, companies, Linkedin profiles, addresses, and more.

By capturing site visitor data, marketing and sales teams can build warm lists, expand and enhance remarketing campaigns, and reach high-intent shoppers in ways they never could.

To install the Website Visitor ID X-Ray Pixel, sign up (for FREE!), go to your dashboard, and navigate to My Automations. 

Select + New Automation and get your pixel. We have easy install options for Google Tag Manager, WordPress, and Shopify, or you can install the pixel manually.

[Learn More About Our B2B Website Visitor ID Capabilities]

#2. B2B Marketing Tool: Sales Handy

SalesHandy is a pretty cool email productivity tool that I’ve only recently learned about. And its email platform does a few things that are not only super useful for B2B marketing and sales but are also rare features to find on one platform.

SalesHandy provides recipient engagement data and a nine-stage automated follow-up process. 

Some of the cool features I mentioned above consist of

Smart email templates

Email attachment tracking tools

Built-in verification to ensure higher email deliverability rates

Most email marketing tools require a third-party vendor to account for all three of the above features.

Marketers use SalesHandy to their benefit by setting behavior-based auto follow-ups, scheduling their emails at the right time, and more. Furthermore, SalesHandy is another email marketing tool that makes sure marketers stay safe from spam filters.

#3. B2B Marketing Tool: Linked Helper

Put your LinkedIn Sales Navigator account on steroids with LinkedHelper’s many automation tools to build connections, send ‘Inmails’, and generate leads. 

By combining LinkedIn Sales Navigator with LinkedHelper, you can:

Acquire thousands of targeted contacts by sending personalized invitations to 2nd & 3rd contacts.

Create an auto-mailing system, auto-responders, and sequenced messages to 1st connections or LinkedIn Group Members.

Automate profile visits & export lead lists to CSV for Google Sheets and MS Excel.

Build targeted mailing lists.

Boost your profile by automating the endorsement of targeted profiles, and receive endorsements in return.

Invite 1st connections to join a LinkedIn Group.

Automatically add your signature to messages, follow or unfollow LinkedIn connections, and withdraw sent pending invitations.

LinkedHelper also has a great lists manager that allows you to build lead generation funnels.

#4. B2B Marketing Tool: Overloop

Overloop is a cloud-based Customer Relationship Management (CRM) solution designed for small to medium-sized businesses. Overloop does a ton of cool things including sales automation, email campaigns, and lead management.

Overloop helps marketers with lead generation in a few ways:

Automated email sequences: Enables sending personalized email campaigns to nurture leads effectively.

Lead scoring: Assesses and ranks leads based on their engagement and potential value.

Integration with marketing platforms: Facilitates seamless data flow from various marketing channels for better lead management.

Real-time analytics: Offers insights into campaign performance and lead behavior for strategic adjustments.

Customizable templates: Provides a range of templates for emails and landing pages to capture leads efficiently.

#5. B2B Marketing Tool: Clearbit

Clearbit has rapidly become one of the most popular B2B marketing tools on the market. It’s a marketing data engine for all of your customer interactions.

The platform can be used to better understand your customers, identify future prospects, and personalize your marketing and sales interactions. It literally cross-references 250+ sources and covers up to 85 unique tags. All marketers need is an email or corporate domain to fill in the rest.

Clearbit has three separate but integrated tools that it’s well known for:

Reveal: Unmask anonymous web traffic to personalize for target accounts

Enrichment: Turn any email or domain into a full person or company profile

Prospector: Discover your ideal accounts and leads with complete contact info

Keep in mind that Clearbit is not a cheap service, and if you don’t know how to use it, you can quickly run up the bill by acquiring too many leads. That’s why you need to use the tool properly, carve out your target audiences, and then harness the platform’s power to consistently fill your sales team’s pipeline with quality leads and data.

[See More B2B Lead Generation Tools]

Sales Enablement Tools

Sales enablement provides marketers with essential resources, analytics, and automation capabilities to enhance sales performance and streamline collaboration between sales and marketing teams.

#6. B2B Marketing Tool: Chilipiper

Chili Piper is a scheduling software that streamlines the process of booking meetings. It also helps convert leads into customers through its advanced appointment setting and calendar management features.

Chili Piper enhances sales enablement for marketers with its targeted features:

Automated Scheduling: Simplifies meeting organization by automatically syncing with sales calendars, reducing back-and-forth communication.

Instant Lead Qualification: Streamlines the process of evaluating leads, ensuring quick follow-up with high-potential prospects.

Integration with CRM and Marketing Tools: Seamlessly connects with existing CRM systems and marketing tools for unified data management and workflow optimization.

#7. B2B Marketing Tool: Seismic

Seismic is a sales enablement platform that focuses on sales productivity through personalized content and insights at scale.

Seismic is great for marketers when it comes to sales enablement for a few reasons:

Content Optimization: Provides tools for creating, managing, and personalizing sales content, ensuring relevance and impact.

Analytics and Insights: Offers detailed analytics on content usage and effectiveness, enabling data-driven decisions and strategies.

Integration with Sales and Marketing Platforms: Seamlessly integrates with various sales and marketing tools, enhancing collaboration and efficiency across teams.

#8. B2B Marketing Tool: Consumer Directory

The Customers.ai intent-based Consumer Directory can help you not only enrich leads data but also build your Facebook retargeting list with customers who fit your ICP.

Consumer Directory assists marketers in sales enablement by offering focused functionalities including:

Targeted Consumer Profiles: Detailed and segmented consumer profiles to help marketers tailor their strategies and messages effectively.

Behavior Insights: Insights into consumer behavior and preferences, enabling more personalized and effective marketing campaigns.

Easy Integration: Seamlessly integrates with various marketing and sales tools, enhancing data-driven decision-making and campaign management.

#9. B2B Marketing Tool: Gong.io

Gong.io is an AI-powered revenue intelligence platform that analyzes customer interactions across calls, emails, and meetings to provide actionable insights for improving sales strategies and performance.

Gong.io boosts sales enablement for marketers through:

Conversation Intelligence: Analyzes sales calls and meetings to provide insights on customer interactions and feedback.

Deal Execution Insights: Offers real-time data on sales processes, helping identify best practices and areas for improvement.

Integration with CRM and Sales Tools: Seamlessly integrates with existing CRM and sales platforms, ensuring a unified approach to sales strategy and execution.

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Account-Based Marketing (ABM)

Account-based marketing is fundamentally different from traditional marketing and often requires a completely different tech stack. ABM focuses on quality, and aims to produce fewer new opportunities for sales, while also closing more deals and larger accounts.

The following tools will prove to be a strong addition to kick your ABM strategy into gear.

#10. B2B Marketing Tool: Cloze

When you’re prospecting dozens or even hundreds of highly targeted relationships, it’s easy to lose track of conversations, even when it’s a promising lead.

Cloze is a relationship management tool built for ABM (account-based marketing) that allows you to see everything about your contacts in one place.

Whether it’s email, phone calls, notes, scheduled meetings, follow-up notifications, or customer profiles, Cloze has you covered.

Cloze learns who’s important to you and reminds you of the things you’re most likely to forget about. Additionally, Cloze prompts you to keep in touch with your contacts and will provide you the proper context to do so automatically.

Cloze automatically keeps track of your email, phone calls, meetings, documents, Evernote, LinkedIn, Facebook, Twitter, and dozens of other services.

And it’s all organized for you by contact, company, meeting, etc.

Cloze even continuously grabs all the email signatures in your inbox in order to make sure your contacts are always up-to-date.

#11. B2B Marketing Tool: Metadata.io

Metadata.io is an AI-powered demand generation platform that helps to optimize ABM campaigns across paid media at scale. 

Although Metadata.io isn’t “cheap” software by any means, it’s a solid option for SMBs that want the capabilities of a Terminus or Demandbase at a cheaper price point.

To use Metadata.io, you’ll first need to connect the tools in your marketing tech stack that hold significant data. For example, you’ll want to connect your CRM along with your social media accounts. 

Once you’ve connected your apps, you can discover and visualize your ideal customer profiles based on your data and use them to create lists of target accounts and personas.

After discovering your ideal customer profiles and creating lists of target accounts to go after, let Metadata’s AI operator execute campaigns for you.

All you need to do is drag-and-drop your marketing creative (images, headlines), content assets (e.g. eBooks, whitepapers), and the audience segments you created in the platform and hit ‘Launch’ to execute hundreds of campaigns at once.

Finally, after running your campaigns through Metadata.io, the platform will enrich leads before syncing them with your CRM. Then use reports and your KPIs dashboards to monitor performance based on pipeline generated, target-account information, existing pipeline impact, and more.

#12. B2B Marketing Tool: Builtwith

BuiltWith is a handy tool and browser extension that can be used to tell you what technology is running on a company’s website.

BuiltWith is great for ABM due to its comprehensive web technology tracking capabilities:

Technology Profiling: Identifies the technologies used by target accounts, enabling personalized and informed outreach strategies.

Market Analysis: Provides insights into technology trends and usage in specific industries, helping tailor ABM campaigns to the target audience.

Lead Generation and Qualification: Offers detailed tech stack data for lead qualification, ensuring marketers focus on high-potential accounts with relevant needs.

#13. B2B Marketing Tool: UberFlip

Uberflip is an account-based marketing tool used to drive pipeline, revenue, and retention through personalized content experiences.

The key to Uberflip is the content experiences it creates:

Personalized Content Hubs: Tailored content destinations for each account, boosting engagement and relevance.

Content Insights: Analytics on how target accounts interact with content, informing strategy and content optimization.

Seamless Integration with ABM Tools: Integrates with popular ABM and marketing platforms, ensuring a cohesive and efficient campaign execution.

[See More Account Based Marketing Tools]

Event and Webinar Software

With the dramatic uptick in virtual summits and online events over the past few years, B2B marketing tools for events and webinars have reached peak importance.

#13. B2B Marketing Tool: Bizzabo

Bizzabo is a holistic platform that provides your team with all the tools they need to create rewarding events, while surfacing insights to help your events grow both on and offline.

With Bizzabo, you can: 

Fully orchestrate your visitor to attendee experience with enriched and stunning forms, multiple ticket types.

Build a branded event website with an editor that’s integrated with your event registration software and event app.

Send email invites and promotional campaigns that drive interest and registrations with the help of personalized content.

Increase audience engagement via push notifications, one-on-one networking, interactive agendas, and live polling all work together both in and outside the mobile event app.

Give your sponsors unique opportunities, including custom splash screens, special offers, automated push notification shout-outs, sponsorship tiers, and the data to accurately measure sponsor ROI.

Bizzabo claims to be different from the competition because their platform has advanced personalization capabilities, account-based marketing specific feature sets, and is “the only event success software on the market.”

#15. B2B Marketing Tool: Everwebinar

If you’re looking for an affordable webinar solution that’s fairly simple to use and will save you tons of time, WebinarJam and EverWebinar working together could be a great solution.

EverWebinar is a great tool to add to your growth marketing strategy because it allows you to create evergreen webinars.   

Same as your brand’s most valuable content that you can share multiple times with your audience – known as evergreen content – EverWebinar facilitates the sharing of the same webinar as many times as you’d like.

The best part of the webinar platform is that the audience always feels like they’re watching the live recording.

For example, after you’ve recorded your webinar, you can add comments, questions, live polls, and more. All of which appear to be asked in real-time to every new viewer. This way, it always feels like you’re one of many audience members actively watching and participating, which increases engagement.

Additionally, you can add countdown timers to your website that makes it look like a brand new webinar is about to start in (for example) 10 minutes. Even though it’s a recording that could have made a year ago, interested site visitors will sign-up with urgency thinking they got there just in time.

So, although EverWebinar is somewhat deceitful, once you get good at using the platform, you’ll be able to generate hundreds and possibly thousands of leads over time with just 1 solid recording.  

#16. B2B Marketing Tool: Zoom

Zoom allows users to host and broadcast online seminars, offering tools for registration, audience interaction, and comprehensive analytics to manage and evaluate webinar success.

Zoom makes webinars easy with:

Interactive Tools: Geatures like polls, Q&A sessions, and chat options to engage audiences and gather feedback.

Customizable Registration: Options for custom registration forms, helping in lead generation and audience segmentation.

Detailed Analytics: Insights on attendee engagement, participation metrics, and post-webinar data for performance assessment and improvement.

#17. B2B Marketing Tool: StreamYard

Streamyard is a live streaming studio that enables users to host interactive webinars and broadcasts. It also has easy-to-use tools for multi-platform streaming, audience engagement, and custom branding.

Streamyard supports B2B marketers in conducting effective webinars through its user-friendly features:

Multi-Platform Broadcasting: Enables streaming across multiple social media platforms simultaneously, expanding audience reach.

Custom Branding Options: Allows users to add custom logos, overlays, and backgrounds to create a professional and branded webinar experience.

Interactive Engagement Tools: Provides features such as on-screen comments and guest participation, enhancing audience interaction and engagement.

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Conversion Rate Optimization (CRO) Software

Next, we’re going to cover some B2B marketing tools that are great for CRO. 

Capitalizing on your website traffic is extremely important due to the increased difficulty to convert and/or capture contact details on a B2B website as opposed to a B2C website. 

Use these tools to increase your conversion rates and save lots of time in the process.

#18. B2B Marketing Tool: Unbounce

Unbounce is one of the most popular landing page builders for B2B marketers with no coding skills required. 

When you’re running highly-targeted campaigns, such as account-based marketing campaigns, you need your entire funnel to be tailored to its audience. This is why custom-built landing pages are so important.

Think about it…

For example, let’s say you click on an ad that’s promoting chatbots for marketing agencies and you landed on the Customers.ai homepage. The homepage doesn’t immediately mention marketing agencies, so the chances of that traffic bouncing are higher than if you had landed on a page that says in big bold letters, “The Best Chatbot Platform For Marketing Agencies.”

When you build your marketing funnels, make sure that people find exactly what they were looking for when they landed on your page. Conversion rates are guaranteed to go up, and search engines will reward you for it.

With Unbounce, you can duplicate your most successful landing pages to save time and build high converting pages in bulk. Furthermore, you can A/B test variations of landing pages to see which messaging and designs perform best.

Lastly, Unbounce’s Conversion Intelligence and Smart Traffic features help to set it apart from the competition.

#19. B2B Marketing Tool: HotJar

Marketers have so much data available to them nowadays that we forget just how much we could learn by watching people explore the website.

Sometimes Google Analytics doesn’t have all the answers. And that’s when heat mapping and screen recording tools like HotJar save the day. 

With HotJar, you can dramatically lessen the need to make assumptions when it comes to your UI/UX.

If you want to find out exactly why your website visitors are not converting, use HotJar. 

Although HotJar’s heatmaps and conversion funnels are extremely useful, I personally have found most of the platform’s value in the video recordings of visitors on my web pages. 

More than once, by watching just 5 or so video recordings each day, I’ve identified opportunities that dramatically increased conversion rates, or found something on the site that was broken, which was costing the company thousands of dollars.

#20. B2B Marketing Tool: Autopilot

Autopilot is all about building a lead generation and lead nurturing process that can then be automated. 

It’s a visual marketing automation tool makes it easy to design personalized customer experiences that capture and convert.

Autopilot allows users to: 

Customize journeys based on audience behavior. 

Build workflows to automatically send messages through multiple channels. 

Generate reports about customer journey progress and more.

The platform comes with a drag-and-drop interface that can automate workflows and personalized messages.

Autopilot also includes features such as measuring campaign ROI and optimizing the revenue funnel based on real-time performance. Lastly, Autopilot integrates with other popular platforms such as Salesforce, InsideView, GoodData, Zapier, Slack, and Twilio.

#21. B2B Marketing Tool: Lucky Orange

Lucky Orange is a conversion optimization tool designed to help businesses track and engage with visitors.

Lucky Orange offers real-time insights through features like heatmaps, surveys, session recordings, and conversion funnels, allowing businesses to understand user behavior, identify pain points, and make data-driven improvements that significantly enhance website performance and user experience.

“In the fast-paced world of e-commerce, Lucky Orange is your backstage pass to see how visitors really use your website. Study behavior to remove conversion blockers and take your store to the next level.”– Molly Staats, Director Of Strategic Partnerships at Lucky Orange

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Social Media Automation Software

The following content marketing tools can handle your social media management, Facebook ads, keyword research, content creation, search performance analytics, and more.

#22. B2B Marketing Tool: Agorapulse

Agorapulse is an all-in-one social media dashboard that simplifies the understanding of social media metrics. And the platform is particularly great for B2B companies.

You can use AgoraPulse to gain audience insights and capitalize on otherwise missed opportunities with full monitoring and engagement tools for social media.

AgoraPulse has many options to build a pipeline for your content calendar. Additionally, you can store unlimited posts tied to particular themes, and queue them up for publishing.

Reporting and analytics are what AgoraPulse does best. 

It’s easy to export reports with key metrics and analysis on engagement and growth, and content reports let you identify posts that achieve the best reach, engagement, and clicks for any given period.

Lastly, you can use AgoraPulse to show your marketing team’s impact on conversions and revenue, as well as encourage more positive engagement with your ads across social channels.

#23. B2B Marketing Tool: Meet Edgar

MeetEdgar is a content recycler that can be used to automate the sharing of all your evergreen content across Facebook, LinkedIn, Twitter, and Instagram.

MeetEdgar streamlines social media management for marketers with:

Content Scheduling: Automates the scheduling and reposting of content across multiple social media platforms, ensuring consistent online presence.

Content Library and Categorization: Maintains an organized library of evergreen content, categorized for easy access and systematic posting.

Performance Analytics: Provides insights into post engagement and reach, helping marketers refine their social media strategies for better results.

#24. B2B Marketing Tool: Social Pilot

SocialPilot is a user-friendly tool for automating and streamlining social media activities, making it easier to schedule posts, manage multiple accounts, and gain insights from social media campaigns.

SocialPilot boosts your social media game with handy tools:

Easy Post Scheduling: Queue up posts for multiple platforms all in one place.

Insightful Analytics: Get a clear view of what’s working and what’s not with your social posts.

Team-Friendly: Makes it a breeze for your whole team to chip in on social media management.

#25. B2B Marketing Tool: Oktopost

Oktopost is a savvy social media tool tailored for B2B marketing, making it easy to handle content scheduling, boost team involvement, and keep track of how well your social strategies are performing.

Oktopost makes social media a breeze for B2B marketers with some cool features:

Easy Posting: Schedule your content across different platforms without a hitch.

Team Power: Get your whole team sharing and amplifying your message.

Analytics that Speak: Dive into clear analytics to see what’s hitting the mark and tweak your game plan.

B2B Content Marketing Software

For B2B marketers, content marketing software is the secret sauce for crafting, tracking, and perfecting content that clicks with business audiences and boosts engagement.

#26. B2B Marketing Tool: Surfer

Surfer is kind of like a GPS for content marketing, guiding you to create SEO-savvy content with tips on keywords, structure, and making your writing more reader-friendly, all to help you climb up those search rankings.

Surfer gives content marketers an edge with its smart, SEO-focused tools:

SEO Content Editor: Offers real-time suggestions for keywords, headings, and structure to boost your content’s search engine appeal.

Content Audit: Quickly analyzes existing pages and provides actionable tips for improvement.

Keyword Research: Delve into keywords, discover what’s trending, and find the sweet spots for your content strategy.

#27. B2B Marketing Tool: Jasper.ai

Jasper.ai is an AI-based tool created to assist content creators, marketers, and ecommerce businesses in crafting high-quality content.

Jasper.ai is a content creator’s dream, jazzing up your marketing with some smart AI tricks:

Quick Content Magic: Whips up quality, original content fast, giving your brain a break.

Tone Tweaker: Easily adjusts to sound just like your brand, whether it’s formal, fun, or somewhere in between.

Content Chameleon: From blog posts to tweets, it switches gears smoothly, keeping your content fresh and engaging.

#28. B2B Marketing Tool: Grammarly

Grammarly is AI-based typing assistant that reviews spelling, grammar, punctuation, clarity, engagement, and more.

Grammarly spices up content marketing with handy tools that keep your writing sharp and engaging:

Error Busting: Sniffs out grammar goofs and style slips, keeping your content smooth and mistake-free.

Tone Matching: Helps you nail the right vibe, whether you’re aiming for friendly, formal, or anything in between.

Originality Check: Keeps your work unique with a plagiarism checker, so you can be sure your content is always fresh.

Making Your B2B Marketing Toolset Work for You

As we leap into 2024, it’s clear that the B2B marketing toolbox is brimming with more gadgets and gizmos than ever before.

From AI wizards like Jasper.ai that sprinkle creative magic on your content, to the analytical acrobats like Grammarly ensuring your prose leaps flawlessly off the page, there’s a tool for every twist and turn in your marketing journey.

So, dear marketers, strap in and gear up – it’s time to turbocharge your strategies, sprinkle some digital dazzle, and write your own success story in the thrilling world of B2B marketing. Let’s make 2024 the year we not only reach for the stars but land amongst them with a toolbox that’s out of this world!

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Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

B2B Marketing Tool FAQs

Q. What are B2B marketing tools?

B2B marketing tools are specialized software and platforms designed to facilitate marketing efforts in a business-to-business environment. They include a range of solutions like CRM systems, email marketing software, analytics tools, and social media management platforms, catering specifically to the needs of businesses targeting other businesses.

Q. Why are B2B marketing tools important?

B2B marketing tools play a vital role in streamlining marketing campaigns, tracking customer engagement, analyzing market data, and enhancing sales and customer relationships in the B2B sector. Their use leads to more effective marketing strategies, better customer insights, and improved ROI.

Q. How do B2B and B2C marketing tools differ?

B2B marketing tools are tailored for the longer sales cycles and more complex decision-making processes typical in business-to-business transactions. In contrast, B2C marketing tools focus on reaching individual consumers, emphasizing personalization and emotional appeal. B2B tools often include more robust CRM and lead nurturing features.

Q. What are the top B2B marketing tools available today?

Some of the top B2B marketing tools include HubSpot for inbound marketing and CRM, LinkedIn Sales Navigator for social selling, Marketo for marketing automation, Google Analytics for web analytics, and Trello or Asana for project management.

Q. How can B2B marketing tools improve lead generation?

B2B marketing tools enhance lead generation by automating and optimizing marketing processes, enabling targeted content distribution, nurturing leads through personalized campaigns, and providing detailed analytics to understand and improve strategies.

Q. What role does CRM play in B2B marketing?

CRM systems are central in B2B marketing, providing a platform to manage and analyze customer interactions and data throughout the customer lifecycle. They help in segmenting audiences, personalizing communication, tracking sales opportunities, and maintaining customer relationships.

Q. Are there specific B2B marketing tools for small businesses?

Yes, there are B2B marketing tools tailored for small businesses, such as Mailchimp for email marketing, Hootsuite for social media management, and Canva for easy-to-use graphic design. These tools are often more affordable and user-friendly, suitable for smaller scale operations.

Q. How do analytics tools enhance B2B marketing?

Analytics tools in B2B marketing provide insights into market trends, customer behavior, and campaign effectiveness. They help marketers make data-driven decisions, tailor strategies to target audiences, and measure ROI more accurately.

Q. What is the importance of email marketing in B2B strategies?

Email marketing is crucial in B2B strategies for direct communication with prospects and customers. It allows for personalized, value-driven content delivery, nurturing leads, and maintaining ongoing customer engagement.

Q. How can social media be used effectively in B2B marketing?

Social media in B2B marketing is effective for brand building, networking, and content distribution. Platforms like LinkedIn are especially valuable for connecting with industry professionals, sharing thought leadership content, and generating leads.

Q. What are the best practices for using B2B marketing tools?

Best practices include understanding your target audience, integrating various tools for a unified marketing strategy, regularly analyzing data for insights, personalizing content, and staying updated with the latest marketing trends and technologies.

Q. Can B2B marketing tools aid in customer retention?

Yes, B2B marketing tools can significantly aid in customer retention by facilitating personalized communication, enabling regular engagement through automated marketing campaigns, and allowing businesses to quickly respond to customer needs and feedback.

Q. How do content marketing tools benefit B2B marketers?

Content marketing tools help B2B marketers in planning, creating, distributing, and analyzing content. They ensure consistent and relevant content delivery, which is key in establishing authority and engaging a business audience.

Q. What is the role of SEO in B2B marketing?

SEO is crucial in B2B marketing for increasing online visibility, driving organic traffic, and generating leads. Effective SEO strategies involve optimizing content with relevant keywords, improving website structure, and building quality backlinks.

Q. How important is mobile optimization for B2B marketing tools?

Mobile optimization is extremely important as an increasing number of business professionals access information and make decisions on mobile devices. B2B marketing tools need to be mobile-friendly to ensure effective engagement and user experience.

Q. What are the challenges in implementing B2B marketing tools?

Challenges include selecting the right tools that align with business goals, integrating different tools for seamless operation, training team members, and ensuring data security and privacy.

Q. How do B2B marketing tools integrate with other business systems?

B2B marketing tools often integrate with other business systems like CRM, ERP, and e-commerce platforms through APIs or built-in integrations. This ensures seamless data flow and improves efficiency in marketing and sales operations.

Q. What is account-based marketing in the context of B2B?

Account-based marketing (ABM) in B2B focuses on targeting specific high-value accounts rather than the broader market. It involves personalized marketing strategies and content tailored to the needs and characteristics of each targeted account.

Q. How can automation improve B2B marketing efforts?

Automation in B2B marketing improves efficiency by handling repetitive tasks like email campaigns, social media posting, and lead nurturing. It allows marketers to focus on strategy and creative aspects, ensuring more targeted and effective campaigns.

Q. What metrics should be tracked using B2B marketing tools?

Key metrics include lead generation rates, conversion rates, website traffic and engagement, email open and click-through rates, social media engagement, and ROI of marketing campaigns.

Q. How does AI influence B2B marketing tools and strategies?

AI influences B2B marketing by enabling predictive analytics, personalized content recommendations, chatbots for customer service, and automating complex data analysis tasks, leading to more effective and efficient marketing strategies.

Q. What are the trends in B2B marketing tools for 2024?

Emerging trends include increased use of AI and machine learning, greater focus on customer experience, the rise of voice search optimization, more sophisticated data analytics tools, and the growing importance of video marketing.

Q. How do B2B marketing tools handle data privacy and security?

B2B marketing tools handle data privacy and security by complying with regulations like GDPR, using secure data storage and transfer methods, implementing user access controls, and regularly updating their security protocols.

Q. Can B2B marketing tools be customized for niche industries?

Yes, many B2B marketing tools offer customization options to cater to specific industries. This includes specialized templates, industry-specific analytics, and integrations with niche-specific software and databases.

Q. How do B2B marketers measure the ROI of their tools?

ROI of B2B marketing tools is measured by analyzing metrics like lead generation, conversion rates, customer acquisition costs, customer lifetime value, and overall impact on sales and revenue growth.

Q. What is the future of B2B marketing tools?

The future of B2B marketing tools lies in further integration of AI and machine learning, enhanced data analytics capabilities, more personalized and automated marketing solutions, and the increasing use of AR/VR technologies for immersive marketing experiences.

Q. How do B2B marketing tools support international marketing?

B2B marketing tools support international marketing by providing features like multi-language support, global market analytics, localization of content, and the ability to manage diverse marketing campaigns across different regions and cultures.

Q. What are common mistakes to avoid when using B2B marketing tools?

Common mistakes include not fully understanding the tool’s capabilities, failing to integrate tools into the broader marketing strategy, neglecting data analysis, overlooking the importance of mobile optimization, and not regularly updating content and strategies.

Q. How can B2B marketing tools aid in competitor analysis?

B2B marketing tools aid in competitor analysis by tracking competitors’ online activities, analyzing their SEO strategies, monitoring their social media presence, and providing insights into their content and advertising approaches.

Q. How should businesses choose the right B2B marketing tools?

Businesses should choose B2B marketing tools based on their specific marketing goals, the size of their business, budget constraints, the tools’ integration capabilities with existing systems, and the specific features needed for their marketing strategies.
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