Despite their competitive performance on knowledge-intensive tasks, large language models (LLMs) still have limitations in memorizing all world knowledge especially long tail knowledge. In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. Existing work has shown that retrieving KG knowledge to enhance LLMs prompting can significantly improve LLMs performance in KGQA. However, their approaches lack a well-formed verbalization of KG knowledge, i.e., they ignore the gap between KG representations and textual representations. To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA. Based on this approach, we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task. Experiments on several KGQA benchmarks show that the proposed KG-to-Text augmented LLMs approach outperforms previous KG-augmented LLMs approaches regarding answer accuracy and usefulness of knowledge statements.

翻译：尽管大语言模型（LLMs）在知识密集型任务中展现出竞争力，但在记忆所有世界知识（尤其是长尾知识）方面仍存在局限。本文研究基于知识图谱（KG）增强的语言模型方法，以解决需要丰富世界知识的知识图谱问答（KGQA）任务。现有工作表明，检索KG知识以增强LLMs提示可显著提升其在KGQA中的表现。然而，这些方法缺乏对KG知识的良好结构化表述，即忽略了KG表示与文本表示之间的鸿沟。为此，我们提出一种答案敏感的KG-to-Text方法，能够将KG知识转化为对KGQA最具信息量的优质文本化陈述。基于该方法，我们构建了面向KGQA任务的KG-to-Text增强大语言模型框架。在多个KGQA基准上的实验表明，所提出的KG-to-Text增强LLMs方法在答案准确性和知识陈述有用性方面均优于以往的KG增强LLMs方法。