Researchers from Nvidia and the University of Illinois at Urbana Champaign introduce Retro 48B, a significantly larger language model than previous retrieval-augmented models like Retro (7.5B parameters). Retro 48B is pre-trained with retrieval on an extensive corpus, leading to improved perplexity. The encoder in InstructRetro can be ablated, suggesting that continued retrieval-augmented pre-training enhances the decoder’s performance in question answering.

Retrieval-augmented language models are well-established for open-domain question answering, benefiting both during pre-training and inference. Their approach reduces model perplexity, improves factuality, and enhances task performance post-fine-tuning. Existing retrieval-augmented models are constrained in size compared to decoder-only models, limiting their zero-shot generalization potential after instruction tuning. Instruction tuning, vital for natural language understanding, has gained support from high-quality datasets like FLAN, OpenAssistant, and Dolly, enabling superior performance in chat and question-answering tasks.

Pretraining language models with retrieval, such as Retro, has shown promise in reducing perplexity and enhancing factual accuracy. However, existing retrieval-augmented models need more parameters and training data, impacting their performance in instruction tuning and other tasks typical of large language models. Their study introduces Retro 48B, the largest retrieval-augmented model, continuing to pretrain a 43B GPT model with additional tokens. InstructRetro, obtained from this process, significantly improves zero-shot question answering compared to traditional GPT models. InstructRetro’s decoder achieves similar results when the encoder is ablated, demonstrating the retrieval-augmented pre-training’s effectiveness in context incorporation for question answering.

Their study explores an extensive process involving pretraining a GPT model to create Retro 48B, instructing it to enhance its zero-shot question-answering abilities, and evaluating its performance in various tasks. It introduces a novel 48B-sized retrieval-augmented language model, InstructRetro, which significantly outperforms the standard GPT model in zero-shot question-answering tasks after instruction tuning. This scaling-up approach demonstrates the potential of larger retrieval-augmented models in natural language understanding.

Retro 48B, a language model pre-trained with retrieval, surpasses the original GPT model in perplexity. After instruction tuning, referred to as InstructRetro, it significantly enhances zero-shot question answering, with an average improvement of 7% on short-form and 10% on long-form QA tasks compared to its GPT counterpart. Surprisingly, InstructRetro’s decoder backbone alone delivers comparable results, indicating the effectiveness of retrieval-based pretraining in context incorporation for QA.

Introducing InstructRetro 48B, the largest retrieval-augmented language model, significantly enhances zero-shot accuracy in a wide range of open-ended QA tasks compared to its GPT counterpart. Pretraining with retrieval using the Retro augmentation method improved perplexity. Their study’s results suggest that continued pre-training with recovery before instruction tuning offers a promising direction for enhancing GPT decoders in QA. Surprisingly, the decoder achieves comparable accuracy, showcasing the effectiveness of pretraining for context incorporation. InstructRetro excels in long-form QA tasks, highlighting retrieval-augmented pretraining’s potential for challenging tasks.

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