Astute RAG Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models

This paper introduces Astute RAG, a novel Retrieval-Augmented Generation (RAG) technique designed to enhance the reliability of Large Language Models (LLMs) by addressing the challenges posed by imperfect retrieval and knowledge conflicts. The research, documented in "Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models," argues that current RAG systems are vulnerable to inaccuracies stemming from irrelevant or misleading retrieved information, which can contradict the LLM's internal knowledge.

Key Points

• An analysis of real-world retrieval scenarios reveals that imperfect retrieval is common, significantly hindering RAG performance. The study used Google Search with web data and found that approximately 70% of retrieved passages lacked direct answers, impacting LLM accuracy.
• The paper identifies "knowledge conflicts" between an LLM's pre-trained knowledge and retrieved external information as a major obstacle. These conflicts arise when the LLM struggles to reconcile discrepancies between sources.
• Astute RAG is proposed as a solution. This method adaptively uses the LLM's internal knowledge, iteratively integrates it with retrieved information while considering source reliability, and finalizes answers based on the most trustworthy information.

Conclusion

• Experimental results using Gemini and Claude models demonstrate that Astute RAG surpasses existing robustness-focused RAG methods. Importantly, it maintains performance comparable to, or exceeding, LLMs without RAG, even when retrieved information is entirely unhelpful. This resilience is attributed to its ability to effectively resolve knowledge conflicts, leading to more dependable LLM outputs.

Source(s):

• Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models