KiRAG：知识驱动的迭代检索器以增强检索增强生成
KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
AI总结：
针对迭代检索增强生成在多跳问答中检索易受干扰且难以适应动态信息需求的问题，提出知识驱动的迭代检索器KiRAG，通过知识三元组迭代检索并融合推理，显著提升检索准确率与F1值。
摘要：
迭代检索增强生成（iRAG）模型为多跳问答（QA）提供了一种有效的方法。然而，它们的检索过程面临两个关键挑战：（1）可能被不相关的文档或事实不准确的思维链所干扰；（2）它们的检索器并非设计用于动态适应多步推理中不断变化的信息需求，这使得难以识别并检索每一步迭代中所需的缺失信息。因此，我们提出了KiRAG，它使用一种知识驱动的迭代检索器模型来增强iRAG的检索过程。具体来说，KiRAG将文档分解为知识三元组，并利用这些三元组进行迭代检索，以实现事实可靠的检索过程。此外，KiRAG将推理整合到检索过程中，以动态识别并检索弥合信息差距的知识，从而有效适应不断变化的信息需求。实验结果表明，KiRAG显著优于现有的iRAG模型，在多跳问答中R@3平均提升9.40%，F1平均提升5.14%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
