让大模型推理过程扎根知识图谱，提升可解释性和准确性
Grounding LLM Reasoning with Knowledge Graphs
- 将大模型每步推理链接到知识图谱，生成可追溯的逻辑链条
- 在特定领域推理任务上比传统方法准确率提升至少26.5%
- 适合需要高可信度推理的应用，如医疗、金融决策
大型语言模型（LLMs）擅长生成自然语言回答，但其输出往往难以验证且缺乏可追溯性。知识图谱（KGs）通过结构化方式表示实体及其关系，为更可靠的推理提供了基础。本文提出一种新框架，将大模型推理过程与知识图谱结合，使每一步推理都对应图结构数据。该方法将中间“思考”转化为与外部知识一致的可解释痕迹。框架融合了链式思维（CoT）、树状思维（ToT）和图状思维（GoT）等多种推理策略，在针对特定领域图推理的GRBench基准上进行评估。实验表明，该方法达到当前最优（SOTA）性能，相比CoT基线至少提升26.5%。除精度外，还分析了步骤深度、分支结构及模型规模对推理质量的影响，揭示了有效推理的关键条件。整体贡献表明，将大模型接地于结构化知识，可在复杂推理任务中同时实现更高准确率与更强可解释性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel at generating natural language answers, yet their outputs often remain unverifiable and difficult to trace. Knowledge Graphs (KGs) offer a complementary strength by representing entities and their relationships in structured form, providing a foundation for more reliable reasoning. We propose a novel framework that integrates LLM reasoning with KGs by linking each step of the reasoning process to graph-structured data. This grounding turns intermediate ``thoughts'' into interpretable traces that remain consistent with external knowledge. Our approach incorporates multiple reasoning strategies, Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT), and is evaluated on GRBench, a benchmark for domain-specific graph reasoning. Our experiments show state-of-the-art (SOTA) performance, with at least 26.5\% improvement over CoT baselines. Beyond accuracy, we analyze how step depth, branching structure, and model size influence reasoning quality, offering insights into the conditions that support effective reasoning. Together, these contributions highlight how grounding LLMs in structured knowledge enables both higher accuracy and greater interpretability in complex reasoning tasks.
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