发表机构
Northeastern University; Kunming University of Science and Technology; NiuTrans Research（东北大学; 昆明理工大学; NiuTrans研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有检索增强生成（RAG）处理多跳问题时缺乏动态分解与有效过滤的缺陷，提出D²F-ReAG范式，通过判断根级推理可靠性自适应控制推理深度，经实验验证其处理复杂多跳问题的有效性。
AI 中文摘要
大语言模型（LLMs）因依赖静态内部知识，常生成不准确的答案。检索增强生成（RAG）通过整合外部知识解决了这一局限，在单跳查询中表现出色，但在需要跨文档推理的多跳问题上效果不佳。现有方法如图结构RAG或问题分解，往往缺乏动态分解与有效过滤，导致效率和准确性较低。为克服这些局限，我们提出多跳推理增强生成的动态分解与过滤（D2F-ReAG），这是一种新型范式，通过判断根级推理的可靠性来自适应控制推理深度：若根推理可靠，模型直接生成答案；否则将问题逻辑分解为子问题，利用子问题得出的经验证的推理结果优化根推理。在三个多跳基准上开展的实验证明了我们的方法处理复杂多跳问题的有效性。
英文摘要
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.
