A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems

Florin Cuconasu, Giovanni Trappolini, Nicola Tonellotto, Fabrizio Silvestri

A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems: 7 upvotes on Hugging Face Daily Papers, #16 of 27 papers on 2024-06-24. Day-by-day upvote history.

Retrieval Augmented Generation (RAG) represents a significant advancement in artificial intelligence combining a retrieval phase with a generative phase, with the latter typically being powered by large language models (LLMs). The current common practices in RAG involve using "instructed" LLMs, which are fine-tuned with supervised training to enhance their ability to follow instructions and are aligned with human preferences using state-of-the-art techniques. Contrary to popular belief, our study demonstrates that base models outperform their instructed counterparts in RAG tasks by 20% on average under our experimental settings. This finding challenges the prevailing assumptions about the superiority of instructed LLMs in RAG applications. Further investigations reveal a more nuanced situation, questioning fundamental aspects of RAG and suggesting the need for broader discussions on the topic; or, as Fromm would have it, "Seldom is a glance at the statistics enough to understand the meaning of the figures".

Paper page on Hugging Face · arXiv

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