知识链：通过从知识图谱学习将知识推理整合到大语言模型中
Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs
AI总结：
本文提出Chain-of-Knowledge框架，通过知识图谱规则挖掘构建KnowReason数据集，并引入试错机制缓解规则过拟合，从而提升大语言模型的知识推理与通用推理能力。
摘要：
大语言模型（LLMs）在各种自然语言处理（NLP）任务中表现出令人印象深刻的能力，这些任务涉及日益复杂的推理。知识推理作为一种主要推理类型，旨在从已有知识中推导出新知识。虽然知识推理已在知识图谱（KGs）背景下得到广泛研究，但在大语言模型中的知识推理仍探索不足。本文提出Chain-of-Knowledge，一个面向知识推理的综合框架，包括数据集构建和模型学习方法。在数据集构建方面，我们通过在知识图谱上进行规则挖掘创建KnowReason。在模型学习方面，我们观察到朴素训练会导致规则过拟合。因此，我们通过一种模拟人类内部知识探索过程的试错机制来增强CoK。我们在KnowReason上进行了大量实验。结果表明，CoK不仅能有效提升大语言模型在知识推理上的能力，也能提升其在通用推理基准上的表现。
英文摘要：
Large Language Models (LLMs) have exhibited impressive proficiency in various natural language processing (NLP) tasks, which involve increasingly complex reasoning. Knowledge reasoning, a primary type of reasoning, aims at deriving new knowledge from existing one.While it has been widely studied in the context of knowledge graphs (KGs), knowledge reasoning in LLMs remains underexplored. In this paper, we introduce Chain-of-Knowledge, a comprehensive framework for knowledge reasoning, including methodologies for both dataset construction and model learning. For dataset construction, we create KnowReason via rule mining on KGs. For model learning, we observe rule overfitting induced by naive training. Hence, we enhance CoK with a trial-and-error mechanism that simulates the human process of internal knowledge exploration. We conduct extensive experiments with KnowReason. Our results show the effectiveness of CoK in refining LLMs in not only knowledge reasoning, but also general reasoning benchmarkms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
