(D)RAGged Into a Conflict: Detecting and Addressing Conflicting Sources in Retrieval-Augmented LLMs

Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing LLMs with relevant and up-to-date information. However, the retrieved sources can often bring conflicting information and it is not clear how models address such discrepancies. In this work, we first point out that knowledge conflicts stem from various reasons and thus require tailored solutions in order to better align model responses to human preferences. To that end, we introduce a novel taxonomy of knowledge conflicts in RAG and define the desired model’s behavior for each category. Additionally, we construct a high-quality benchmark by asking two expert annotators to identify the conflict type within realistic RAG instances, each comprising a query and its associated search results. Finally, we conduct extensive experiments and show that explicitly informing LLMs about the potential conflict category significantly improves the quality and appropriateness of the responses. Yet, there is still a vast room for improvement. Taken together, our work highlights the importance of evaluating RAG systems not only on factual accuracy but also on their ability to manage and resolve knowledge conflicts effectively.