用知识图谱增强大模型推理链，提升复杂任务准确率。
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models
- 用知识图谱约束推理过程，让思考更可信。
- 检索相关案例并融入推理，准确率最高提升44.3%。
- 将推理转为伪代码形式，逻辑更严谨，适合工程落地。
链式思维（CoT）推理虽能提升大语言模型在复杂任务上的表现，但存在两大缺陷：完全依赖模型生成的推理链可靠性不足；自然语言提示的推理效果低于代码提示。为此，我们提出CoT-RAG框架，包含三项创新设计：(i) 基于知识图谱的链式思维生成，利用知识图谱引导模型生成更可信的推理链；(ii) 可学习的知识案例感知RAG，将检索增强生成融入知识图谱，检索相关子案例与子描述，提供可学习信息；(iii) 伪程序提示执行，引导模型以伪代码形式执行推理，增强逻辑严谨性。在九个涵盖三类推理任务的公开数据集上评估显示，相比现有最优方法，准确率提升4.0%至44.3%。在四个领域特定数据集上的测试也验证了其高精度与高效执行能力，凸显其实际应用价值与可扩展性。代码与数据已开源。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) reasoning boosts large language models' (LLMs) performance on complex tasks but faces two key limitations: a lack of reliability when solely relying on LLM-generated reasoning chains and lower reasoning performance from natural language prompts compared with code prompts. To address these issues, we propose CoT-RAG, a novel reasoning framework with three key designs: (i) Knowledge Graph-driven CoT Generation, featuring knowledge graphs to modulate reasoning chain generation of LLMs, thereby enhancing reasoning credibility; (ii) Learnable Knowledge Case-aware RAG, which incorporates retrieval-augmented generation (RAG) into knowledge graphs to retrieve relevant sub-cases and sub-descriptions, providing LLMs with learnable information; (iii) Pseudo Program Prompting Execution, which promotes greater logical rigor by guiding LLMs to execute reasoning tasks as pseudo-programs. Evaluations on nine public datasets spanning three reasoning tasks reveal significant accuracy gains-ranging from 4.0% to 44.3%-over state-of-the-art methods. Furthermore, tests on four domain-specific datasets demonstrate exceptional accuracy and efficient execution, underscoring its practical applicability and scalability. Our code and data are available at https: //github.com/hustlfy123/CoT-RAG.
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