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

Abstract: Retrieval augmented generation (RAG), while effectively integrating external knowledge to address the inherent limitations of LLMs, can be hindered by imperfect retrieval that contain irrelevant, misleading, or even malicious information. Previous studies have rarely connected the behavior of RAG through joint analysis, particularly regarding error propagation coming from imperfect retrieval and potential conflicts between LLMs' internal knowledge and external sources. Through comprehensive and controlled analyses under realistic conditions, we find that imperfect retrieval augmentation is inevitable, common, and harmful. We identify the knowledge conflicts between LLM-internal and external knowledge from retrieval as a bottleneck to overcome imperfect retrieval in the post-retrieval stage of RAG. To address this, we propose Astute RAG, a novel RAG approach designed to be resilient to imperfect retrieval augmentation. It adaptively elicits essential information from LLMs' internal knowledge, iteratively consolidates internal and external knowledge with source-awareness, and finalizes the answer according to information reliability. Our experiments with Gemini and Claude demonstrate the superior performance of Astute RAG compared to previous robustness-enhanced RAG approaches. Specifically, Astute RAG is the only RAG method that achieves performance comparable to or even surpassing conventional use of LLMs under the worst-case scenario. Further analysis reveals the effectiveness of Astute RAG in resolving knowledge conflicts, thereby improving the trustworthiness of RAG.

• The paper presents a novel RAG framework that integrates internal and external knowledge to overcome retrieval inaccuracies.
• It demonstrates through experiments on datasets like NQ, TriviaQA, BioASQ, and PopQA that up to 70% of passages can be misleading.
• The results indicate enhanced large language model reliability and offer actionable insights for advancing adaptive knowledge integration.

Analyzing Imperfect Retrieval Augmentation and Knowledge Conflicts for LLMs

The paper "Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for LLMs" presents a detailed examination of the challenges associated with retrieval-augmented generation (RAG) in the context of LLMs. This work addresses the critical issue of imperfect retrieval which can introduce irrelevant or misleading information, thereby affecting the reliability of LLMs' responses.

Key Findings and Methodology

The study identifies the occurrence of imperfect retrieval augmentation as a significant impediment in RAG systems. Through controlled experiments on datasets such as NQ, TriviaQA, BioASQ, and PopQA, it highlights how a substantial portion of retrieved passages (up to 70%) do not contain correct answers. This underscores the necessity to develop methods that can handle such imperfections.

In response to these challenges, the authors introduce a novel RAG framework designed to enhance the reliability of LLMs by leveraging both internal and external knowledge. Their approach involves an adaptive generation mechanism to extract relevant information from the LLM's internal knowledge base and a source-aware consolidation process to synthesize this with external data. The framework is designed to resolve conflicts between internal and external information, consolidating consistent information and disregarding misleading content.

Experimental Results

The proposed method was tested using advanced LLMs like Gemini and Claude. The results show a significant improvement in robustness compared to previous methods. The paper reports that the new approach not only exceeds the performance of existing RAG methods in typical scenarios but also in worst-case situations where all retrieved passages are unhelpful. This ability to maintain accuracy highlights the effectiveness of the framework in addressing knowledge conflicts.

Implications and Future Directions

The findings have substantial implications for both theoretical research and practical applications of AI. By demonstrating that LLMs can be made more resilient to retrieval errors, the paper suggests pathways for enhancing the trustworthiness of AI systems deployed in sensitive domains where data reliability is paramount.

The study paves the way for further exploration into adaptive knowledge integration techniques, emphasizing the potential benefits of refining LLM-internal knowledge elicitation and external information synthesis processes. Future work could explore extending this methodology to multimodal settings or applying it to less advanced LLMs to assess broader applicability.

Overall, the research provides a comprehensive framework for addressing the complexities of imperfect retrieval in LLMs, marking a step towards more reliable and trustworthy AI systems.