用推理树结构提升多跳问答的准确性和连贯性。
Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
- 构建显式推理树，分离已知与未知实体，减少错误分解。
- 通过自底向上的迭代重写，证据收集准确率提升7.0% F1。
- 适合需要精准逻辑链的复杂问答场景，如知识密集型任务。
检索增强生成（RAG）在提升大语言模型处理复杂多跳问答任务方面表现出显著效果。现有方法依赖大模型自我引导规划多步检索路径，常因查询分解不准确导致推理连贯性差与错误传播。为此，本文提出推理树引导的RAG（RT-RAG），一种新型分层框架。该框架将多跳问题系统性分解为显式推理树，通过结构化实体分析和基于共识的树选择，明确区分核心问题、已知实体与未知实体，降低误分解风险。随后采用自底向上的遍历策略，通过迭代查询重写与优化，逐步获取高质量证据，有效缓解错误传播。大量实验表明，RT-RAG相比当前最优方法在F1上提升7.0%，在EM上提升6.0%，充分验证其在复杂多跳问答中的有效性。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has demonstrated significant effectiveness in enhancing large language models (LLMs) for complex multi-hop question answering (QA). For multi-hop QA tasks, current iterative approaches predominantly rely on LLMs to self-guide and plan multi-step exploration paths during retrieval, leading to substantial challenges in maintaining reasoning coherence across steps from inaccurate query decomposition and error propagation. To address these issues, we introduce Reasoning Tree Guided RAG (RT-RAG), a novel hierarchical framework for complex multi-hop QA. RT-RAG systematically decomposes multi-hop questions into explicit reasoning trees, minimizing inaccurate decomposition through structured entity analysis and consensus-based tree selection that clearly separates core queries, known entities, and unknown entities. Subsequently, a bottom-up traversal strategy employs iterative query rewriting and refinement to collect high-quality evidence, thereby mitigating error propagation. Comprehensive experiments show that RT-RAG substantially outperforms state-of-the-art methods by 7.0% F1 and 6.0% EM, demonstrating the effectiveness of RT-RAG in complex multi-hop QA.
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