用知识三元组迭代检索，让多跳问答更准更稳
KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
- 将文档拆成知识三元组，逐轮精准检索
- 检索准确率提升9.40%，答案正确率提高5.14%
- 适合需要复杂推理的问答系统研究者
迭代式检索增强生成（iRAG）模型在多跳问答任务中表现优异，但其检索过程面临两大挑战：一是易受无关文档或事实错误的思维链干扰；二是检索器无法动态适应多步推理中不断变化的信息需求，难以识别和获取每一步所需的缺失信息。为此，我们提出KiRAG，一种基于知识驱动的迭代检索器，通过将文档分解为知识三元组，并利用三元组进行迭代检索，实现更可靠的检索过程。同时，将推理机制融入检索流程，动态识别并检索填补信息缺口的知识，有效适配推理过程中的信息演化需求。实验结果表明，KiRAG显著优于现有iRAG模型，在多跳问答任务上，平均检索准确率（R@3）提升9.40%，F1分数提升5.14%。
原文摘要 · Abstract (English)
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.
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