Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. It presents two key challenges: generating correct reasoning paths in response to the complex user queries, and accurately retrieving essential knowledge in the face of potential limitations in large language models (LLMs). Existing approaches primarily rely on prompt-based methods to generate reasoning paths, which are further combined with traditional sparse or dense retrieval to produce the final answer. However, the generation of reasoning paths commonly lacks effective control over the generative process, thus leading the reasoning astray. Meanwhile, the retrieval methods over-rely on knowledge matching or similarity scores rather than evaluating the practical utility of the information, resulting in retrieving homogeneous or non-useful information. Therefore, we propose a Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator framework named SEARCH-R. Specifically, SEARCH-R trains an end-to-end reasoning path navigator, which is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model. Moreover, a novel dependency tree-based retrieval is designed to evaluate the informational contribution of the document quantitatively. Extensive experiments on three challenging multi-hop datasets validate the effectiveness of the proposed framework. The code and dataset are available at: https://github.com/Applied-Machine-Learning-Lab/ACL2026_SEARCH-R.

翻译：多跳问答旨在回答需要多步推理的问题，其面临两大关键挑战：针对复杂用户查询生成正确的推理路径，以及在大型语言模型潜在局限性下准确检索必要知识。现有方法主要依赖基于提示的方法生成推理路径，再结合传统稀疏或稠密检索生成最终答案。然而，推理路径生成过程普遍缺乏有效控制，导致推理偏离正确方向；同时检索方法过度依赖知识匹配或相似度分数，而非评估信息的实际效用，导致检索到同质化或无用的信息。为此，我们提出名为SEARCH-R的结构化实体感知检索与链式推理导航框架。具体而言，SEARCH-R训练端到端推理路径导航器，通过微调Llama3.1-8B模型实现强大的子问题分解器；此外，创新性地设计了基于依赖树的检索方法，可定量评估文档的信息贡献度。在三个具有挑战性的多跳数据集上的大量实验验证了所提框架的有效性。代码与数据集见：https://github.com/Applied-Machine-Learning-Lab/ACL2026_SEARCH-R。