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

Retrieval-augmented generation (RAG), while effective in integrating external knowledge to enhance large language models (LLMs), can be undermined by imperfect retrieval, which may introduce irrelevant, misleading, or even malicious information. Despite its importance, previous studies have rarely explored the behavior of RAG with errors from imperfect retrieval, and how potential conflicts arise between the LLMs' internal knowledge and external sources. We show that imperfect retrieval augmentation might be inevitable and quite harmful, through controlled analysis under realistic conditions. Knowledge conflicts between LLM-internal and external knowledge from retrieval is a bottleneck to overcome in the post-retrieval stage of RAG. To render LLMs resilient to imperfect retrieval, we propose ASTUTE RAG, a novel RAG approach that 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 that ASTUTE RAG significantly outperforms previous robustness-enhanced RAG methods. Notably, ASTUTE RAG is the only approach that matches or exceeds the performance of LLMs without RAG under worst-case scenarios. ASTUTE RAG effectively resolves knowledge conflicts, improving the reliability and trustworthiness of RAG systems. 45.4% LLM correct RAG correct LLM incorrect RAG incorrect Both sides are wrong. It is hard to improve, but combining internal and external knowledge may help. Previous work leverages RAG to address LLMs' knowledge gap. Zonia receives from Reuben a letter in the play. Zonia receives from Reuben a kiss in the play. It is also crucial to mitigate RAG errors with LLMs' internal knowledge.