让大模型主动反思并迭代推理知识图谱，提升可解释性与准确性。
Learning to Retrieve and Reason on Knowledge Graph through Active Self-Reflection
- 通过特殊标记主动判断是否需检索知识，实现动态决策。
- 引入反思机制对检索结果进行批判性评估，支持多轮迭代推理。
- 模型路径高度可解释，适合需要透明推理的场景。
大量研究探索了将大语言模型（LLMs）与知识图谱结合以增强推理能力，但如何理解模型利用结构化图谱知识进行推理仍缺乏深入研究。现有方法通常依赖LLM或检索器对知识使用做出二元判断，过于粗略，且缺乏全程反馈与修正机制。本文提出面向知识图谱推理的主动自反思框架（ARG），首次实现基于结构化图谱的端到端迭代推理训练。模型通过特殊标记主动决定是否需要知识检索，并基于检索结果进行反思性批判，进而迭代推理。生成的推理路径具备高可解释性，便于深入分析模型对结构化知识的理解。最终，该模型在知识图谱推理任务中显著优于现有基线。
原文摘要 · Abstract (English)
Extensive research has investigated the integration of large language models (LLMs) with knowledge graphs to enhance the reasoning process. However, understanding how models perform reasoning utilizing structured graph knowledge remains underexplored. Most existing approaches rely on LLMs or retrievers to make binary judgments regarding the utilization of knowledge, which is too coarse. Meanwhile, there is still a lack of feedback mechanisms for reflection and correction throughout the entire reasoning path. This paper proposes an Active self-Reflection framework for knowledge Graph reasoning ARG, introducing for the first time an end-to-end training approach to achieve iterative reasoning grounded on structured graphs. Within the framework, the model leverages special tokens to \textit{actively} determine whether knowledge retrieval is necessary, performs \textit{reflective} critique based on the retrieved knowledge, and iteratively reasons over the knowledge graph. The reasoning paths generated by the model exhibit high interpretability, enabling deeper exploration of the model's understanding of structured knowledge. Ultimately, the proposed model achieves outstanding results compared to existing baselines in knowledge graph reasoning tasks.
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