检索-重写-回答：一种面向知识图谱问答的KG-to-Text增强大语言模型框架
Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering
AI总结：
本文针对大语言模型在知识图谱问答中长尾知识不足的问题，提出答案敏感的KG-to-Text方法及增强LLMs框架，将KG知识转化为高信息量文本，并在多个基准上验证其优于已有KG增强方法。
摘要：
尽管大语言模型（LLMs）在知识密集型任务上表现出具有竞争力的性能，但它们在记忆所有世界知识尤其是长尾知识方面仍存在局限。本文研究了用于解决需要丰富世界知识的知识图谱问答（KGQA）任务的KG增强语言模型方法。已有工作表明，检索KG知识以增强LLMs提示可以显著提升LLMs在KGQA中的性能。然而，这些方法缺乏对KG知识的良好形式化表达，即它们忽略了KG表示与文本表示之间的差距。为此，我们提出了一种答案敏感的KG-to-Text方法，能够将KG知识转化为对KGQA最具信息量的良好文本化陈述。基于该方法，我们提出了一个KG-to-Text增强的LLMs框架来解决KGQA任务。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优于以往的KG增强LLMs方法。
英文摘要：
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.
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