KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation

TL;DR

KiRAG introduces a knowledge-driven iterative retrieval method that decomposes documents into triples and incorporates reasoning to improve multi-hop question answering accuracy.

Contribution

It presents a novel knowledge-driven retriever that dynamically adapts to reasoning needs and enhances retrieval reliability in multi-hop QA tasks.

Findings

Achieves 9.40% higher R@3 compared to existing models.

Improves F1 score by 5.14% on multi-hop QA.

Demonstrates significant performance gains over prior iRAG approaches.

Abstract

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.