动态分解与过滤提升大模型多跳推理生成准确率
D$^2$F-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation
- 根据根推理可靠性动态决定是否分解问题
- 在三个基准测试上显著提升多跳问答准确率
- 适合需要复杂推理的智能问答系统使用
大型语言模型（LLMs）因依赖静态内部知识，常产生不准确答案。检索增强生成（RAG）通过整合外部知识，虽在单跳查询中表现优异，但在需要跨文档推理的多跳问题上表现不佳。现有方法如图结构RAG或问题分解，常缺乏动态分解与有效过滤，导致效率和准确率较低。为此，我们提出一种新范式D2F-ReAG：通过判断根级推理的可靠性，自适应控制推理深度。若根推理可靠，则直接生成答案；否则将问题逻辑分解为子问题，利用子问题验证后的推理结果来优化根推理。在三个多跳基准测试上的实验表明，该方法能有效处理复杂多跳问题。
原文摘要 · Abstract (English)
Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge. Retrieval-augmented generation (RAG) addresses this limitation by integrating external knowledge and excelling at single-hop queries. However, it struggles with multi-hop questions that require cross-document reasoning. Existing methods, such as graph structured RAG or question decomposition, often lack dynamic decomposition and effective filtering, which leads to lower efficiency and accuracy. To overcome these limitations, we propose Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning. If the root reasoning is reliable, the model directly generates the answer. Otherwise, the question is logically decomposed into sub-questions, and the verified reasoning derived from these sub-questions is used to refine the root reasoning. Experiments on three multi-hop benchmarks demonstrate the effectiveness of our method in handling complex multi-hop questions.
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