让大模型在知识图谱上自动规划并纠错，提升推理准确率。
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs
- 分步分解问题，动态探索知识图谱路径
- 通过记忆与反思机制实现错误路径自我纠正
- 适合需要高可靠性的复杂问答场景
大型语言模型（LLMs）在复杂任务中展现出强大的推理能力，但仍面临知识过时、幻觉和决策过程不透明的问题。相比之下，知识图谱（KGs）能为大模型提供显式且可编辑的知识以缓解这些问题。现有基于知识图谱增强大模型的方法需手动预设探索范围，且要求知识图谱导航完全准确，无法根据问题语义自适应调整探索路径，也无法自我纠正错误推理路径，导致效率与效果受限。为此，本文提出一种新型自校正自适应规划范式——计划-图（Plan-on-Graph, PoG），先将问题分解为多个子目标，再循环执行自适应探索路径、更新记忆、反思是否需自我纠正错误路径的过程，直至得出答案。具体设计了引导（Guidance）、记忆（Memory）和反思（Reflection）三大机制协同工作，保障图推理中自校正规划的自适应广度。在三个真实世界数据集上的大量实验表明，PoG在有效性和效率方面均表现优异。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and editable knowledge for LLMs to alleviate these issues. Existing paradigm of KG-augmented LLM manually predefines the breadth of exploration space and requires flawless navigation in KGs. However, this paradigm cannot adaptively explore reasoning paths in KGs based on the question semantics and self-correct erroneous reasoning paths, resulting in a bottleneck in efficiency and effect. To address these limitations, we propose a novel self-correcting adaptive planning paradigm for KG-augmented LLM named Plan-on-Graph (PoG), which first decomposes the question into several sub-objectives and then repeats the process of adaptively exploring reasoning paths, updating memory, and reflecting on the need to self-correct erroneous reasoning paths until arriving at the answer. Specifically, three important mechanisms of Guidance, Memory, and Reflection are designed to work together, to guarantee the adaptive breadth of self-correcting planning for graph reasoning. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of PoG.
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