在图上解码：通过生成良构链在知识图谱上实现忠实且可靠的推理
Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains
AI总结：
本文提出无需训练的DoG框架，通过图感知约束解码让LLM在知识图谱上生成良构事实链，从而为KGQA提供忠实可靠的推理轨迹，并在多任务、多模型上表现优异。
摘要：
知识图谱（KGs）以其结构化的知识表示，可作为问答（QA）的可靠知识来源。现有关于将KG用于大型语言模型（LLMs）的研究普遍依赖子图检索器或迭代提示，忽视了LLMs逐步推理能力与KG结构特性之间潜在的协同作用。本文提出DoG（Decoding on Graphs，在图上解码），这是一个促进LLMs与KG深度协同的新颖框架。我们首先定义了良构链这一概念，它由KG上一系列相互关联的事实三元组组成，从问题实体出发并通向答案。我们认为，该概念可作为在KGQA中进行忠实且可靠推理的原则。为使LLMs能够生成良构链，我们提出图感知约束解码，其中由KG拓扑导出的约束会规范LLMs的解码过程。该约束解码方法在充分利用LLMs逐步推理能力的同时，确保生成良构链。基于上述内容，DoG作为一种无需训练的方法，能够提供以KG为基础的忠实且可靠的推理轨迹。在具有不同背景KG的多种KGQA任务上的实验表明，DoG取得了优越且稳健的性能。DoG还在多种开源LLMs上展现出广泛适用性。
英文摘要：
Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) due to their structured representation of knowledge. Existing research on the utilization of KG for large language models (LLMs) prevalently relies on subgraph retriever or iterative prompting, overlooking the potential synergy of LLMs' step-wise reasoning capabilities and KGs' structural nature. In this paper, we present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs. We first define a concept, well-formed chain, which consists of a sequence of interrelated fact triplets on the KGs, starting from question entities and leading to answers. We argue that this concept can serve as a principle for making faithful and sound reasoning for KGQA. To enable LLMs to generate well-formed chains, we propose graph-aware constrained decoding, in which a constraint derived from the topology of the KG regulates the decoding process of the LLMs. This constrained decoding method ensures the generation of well-formed chains while making full use of the step-wise reasoning capabilities of LLMs. Based on the above, DoG, a training-free approach, is able to provide faithful and sound reasoning trajectories grounded on the KGs. Experiments across various KGQA tasks with different background KGs demonstrate that DoG achieves superior and robust performance. DoG also shows general applicability with various open-source LLMs.
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。
