大型语言模型与知识图谱在问答中的结合：综述与机遇
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
AI总结：
本文系统综述了大型语言模型与知识图谱结合用于问答的方法，提出结构化分类法，分析各类方法的优势、局限与知识图谱需求，并总结进展、评估指标、基准数据集及未来挑战与机遇。
摘要：
大型语言模型（LLMs）因其在自然语言理解和生成方面的卓越能力，在问答（QA）任务上展现了显著性能。然而，基于LLM的问答在复杂QA任务中仍面临推理能力不足、知识过时和幻觉等问题。近期多项工作将LLMs与知识图谱（KGs）相结合用于QA，以应对上述挑战。在本综述中，我们提出一种新的结构化分类法，根据QA的类别以及知识图谱在与LLMs集成时所扮演的角色，对LLMs与KGs结合用于QA的方法进行分类。我们系统综述了LLMs与KGs结合用于QA的最新方法，并从优势、局限性和知识图谱需求等方面对这些方法进行比较和分析。随后，我们将这些方法与QA任务相对齐，并讨论它们如何应对不同复杂QA的主要挑战。最后，我们总结了相关进展、评估指标和基准数据集，并指出了尚待解决的挑战与机遇。
英文摘要：
Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and generation. However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to address the above challenges. In this survey, we propose a new structured taxonomy that categorizes the methodology of synthesizing LLMs and KGs for QA according to the categories of QA and the KG's role when integrating with LLMs. We systematically survey state-of-the-art methods in synthesizing LLMs and KGs for QA and compare and analyze these approaches in terms of strength, limitations, and KG requirements. We then align the approaches with QA and discuss how these approaches address the main challenges of different complex QA. Finally, we summarize the advancements, evaluation metrics, and benchmark datasets and highlight open challenges and opportunities.
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。
