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.

尽管大型语言模型在知识密集型任务上表现出竞争力，但它们仍存在于记忆所有世界知识，尤其是长尾知识方面的局限性。本文研究了用于解决知识图谱问答任务的知识图增强语言模型方法。基于回答敏感的 KG-to-Text 方法，我们提出了一种将 KG 知识转化为对 KGQA 最有信息量的文本化陈述的方法，并基于此方法提出了一种 KG-to-Text 增强的 LLMs 框架来解决 KGQA 任务。在几个 KGQA 基准测试上的实验证明了所提出的 KG-to-Text 增强 LLMs 方法在回答准确性和知识陈述的有用性方面优于先前的 KG 增强 LLMs 方法。