Large language models have recently emerged as powerful tools for various natural language understanding and image classification tasks. However, these LLMs have challenges, particularly regarding prompt brittleness and multiple biases in the input. These biases can stem from formatting, choice of verbalizers, and the examples used for in-context learning. These issues can lead to unexpected performance degradation, so addressing them effectively is imperative.
Existing efforts to tackle these challenges have given rise to calibration methods to mitigate the biases and recover LLM performance. These methods have sought a more unified view of the problem while addressing its nuances. The need for such solutions is underscored by the fact that LLMs are sensitive to how they are prompted, and their predictions can be influenced by the choice of templates and verbalizers, as well as the order and content of ICL examples.
A team of Google researchers has proposed a new approach called Batch Calibration (BC). BC is a straightforward yet intuitive method that targets explicit contextual bias in the batched input. Unlike other calibration methods, BC is zero-shot and only applied during the inference phase, incurring minimal additional computational costs. This approach can be extended to a few-shot setup, allowing it to adapt and learn contextual bias from labeled data.
The effectiveness of BC is demonstrated through extensive experimentation across more than ten natural language understanding and image classification tasks. In both zero-shot and few-shot learning scenarios, BC outperforms previous calibration baselines. Its simplicity in design and the ability to learn from limited labeled data make it a practical solution for addressing prompt brittleness and bias in LLMs.
The metrics obtained through these experiments show that BC offers state-of-the-art performance, making it a promising solution for those working with LLMs. By mitigating bias and improving robustness, BC streamlines the process of prompt engineering and allows for more efficient and reliable performance from these powerful language models.
In conclusion, the challenges of prompt brittleness and biases in large language models are effectively tackled through innovative calibration methods like Batch Calibration (BC). These methods offer a unified approach to mitigating contextual bias and improving LLM performance. As natural language understanding and image classification continue to evolve, solutions like BC will play a vital role in harnessing the full potential of LLMs while minimizing the impact of biases and brittleness in their responses.
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The post Can We Overcome Prompt Brittleness in Large Language Models? Google AI Introduces Batch Calibration for Enhanced Performance appeared first on MarkTechPost.