HANRAG：面向多跳问答的启发式精准抗噪检索增强生成

HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering

September 8, 2025

作者: Duolin Sun, Dan Yang, Yue Shen, Yihan Jiao, Zhehao Tan, Jie Feng, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu

cs.AI

摘要

检索增强生成（RAG）方法通过将信息检索（IR）技术与大型语言模型（LLMs）相结合，提升了问答系统和对话生成任务的表现。该策略通过从外部知识库中检索信息来增强生成模型的响应能力，已取得了一定成效。然而，当前RAG方法在处理多跳查询时仍面临诸多挑战。例如，某些方法过度依赖迭代检索，在复合查询上浪费了过多检索步骤。此外，使用原始复杂查询进行检索可能无法捕捉到与特定子查询相关的内容，导致检索结果中存在噪声。如果不对噪声进行管理，可能会引发噪声累积问题。为解决这些问题，我们提出了HANRAG，一种基于启发式的新框架，旨在高效应对不同复杂程度的问题。在强大启发机制的驱动下，HANRAG能够路由查询、将其分解为子查询，并从检索文档中过滤噪声。这增强了系统的适应性和抗噪能力，使其能够出色处理多样化查询。我们在多个基准测试中将所提框架与其他行业领先方法进行了对比。结果表明，我们的框架在单跳和多跳问答任务中均取得了卓越性能。

English

The Retrieval-Augmented Generation (RAG) approach enhances question-answering systems and dialogue generation tasks by integrating information retrieval (IR) technologies with large language models (LLMs). This strategy, which retrieves information from external knowledge bases to bolster the response capabilities of generative models, has achieved certain successes. However, current RAG methods still face numerous challenges when dealing with multi-hop queries. For instance, some approaches overly rely on iterative retrieval, wasting too many retrieval steps on compound queries. Additionally, using the original complex query for retrieval may fail to capture content relevant to specific sub-queries, resulting in noisy retrieved content. If the noise is not managed, it can lead to the problem of noise accumulation. To address these issues, we introduce HANRAG, a novel heuristic-based framework designed to efficiently tackle problems of varying complexity. Driven by a powerful revelator, HANRAG routes queries, decomposes them into sub-queries, and filters noise from retrieved documents. This enhances the system's adaptability and noise resistance, making it highly capable of handling diverse queries. We compare the proposed framework against other leading industry methods across various benchmarks. The results demonstrate that our framework obtains superior performance in both single-hop and multi-hop question-answering tasks.