让小模型分步查知识图谱，提升数学推理准确率
Graph-Augmented Reasoning: Evolving Step-by-Step Knowledge Graph Retrieval for LLM Reasoning
- 分步检索知识图谱，边推理边查相关数学知识
- 小模型在数学题上准确率提升20.73%（相对）
- 无需训练，适合资源受限的部署场景
尽管大型语言模型在推理任务中取得成功，但其仍面临领域知识有限、易产生幻觉以及推理深度不足等问题，尤其在资源受限环境下部署的小规模模型更为明显。本文首次探索将逐步式知识图谱检索与逐步推理相结合，提出一种新型范式——图增强推理（Graph-Augmented Reasoning）。目标是使冻结的小规模语言模型能够以分步方式检索并处理相关数学知识，从而在不增加训练成本的前提下提升解题能力。为此，我们提出KG-RAR框架，包含面向过程的知识图谱构建、分层检索策略，以及通用的后检索处理与奖励模型（PRP-RM），用于精炼检索结果并评估每一步推理。在Math500和GSM8K两个基准上的实验表明，该方法在六种模型上均表现良好，其中在Llama-3B上于Math500上实现20.73%的相对性能提升。
原文摘要 · Abstract (English)
Recent large language model (LLM) reasoning, despite its success, suffers from limited domain knowledge, susceptibility to hallucinations, and constrained reasoning depth, particularly in small-scale models deployed in resource-constrained environments. This paper presents the first investigation into integrating step-wise knowledge graph retrieval with step-wise reasoning to address these challenges, introducing a novel paradigm termed as graph-augmented reasoning. Our goal is to enable frozen, small-scale LLMs to retrieve and process relevant mathematical knowledge in a step-wise manner, enhancing their problem-solving abilities without additional training. To this end, we propose KG-RAR, a framework centered on process-oriented knowledge graph construction, a hierarchical retrieval strategy, and a universal post-retrieval processing and reward model (PRP-RM) that refines retrieved information and evaluates each reasoning step. Experiments on the Math500 and GSM8K benchmarks across six models demonstrate that KG-RAR yields encouraging results, achieving a 20.73\% relative improvement with Llama-3B on Math500.
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