KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
This addresses retrieval challenges in multi-hop QA, offering a domain-specific incremental improvement.
The paper tackles the problem of irrelevant documents and inaccurate reasoning disrupting retrieval in iterative retrieval-augmented generation (iRAG) for multi-hop question answering, proposing KiRAG which improves average R@3 by 9.40% and F1 by 5.14% over existing models.
Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multi-hop question answering (QA). However, their retrieval process faces two key challenges: (1) it can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts; (2) their retrievers are not designed to dynamically adapt to the evolving information needs in multi-step reasoning, making it difficult to identify and retrieve the missing information required at each iterative step. Therefore, we propose KiRAG, which uses a knowledge-driven iterative retriever model to enhance the retrieval process of iRAG. Specifically, KiRAG decomposes documents into knowledge triples and performs iterative retrieval with these triples to enable a factually reliable retrieval process. Moreover, KiRAG integrates reasoning into the retrieval process to dynamically identify and retrieve knowledge that bridges information gaps, effectively adapting to the evolving information needs. Empirical results show that KiRAG significantly outperforms existing iRAG models, with an average improvement of 9.40% in R@3 and 5.14% in F1 on multi-hop QA.