arXiv 2404.042642024-12-12cs.IRcs.AI

Logic Query of Thoughts：引导大语言模型借助知识图谱回答复杂逻辑查询

Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs

University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Hong Kong University of Science and Technology（香港科技大学）

Lihui Liu, Zihao Wang, Ruizhong Qiu, Yikun Ban, Eunice Chan, Yangqiu Song, Jingrui He, Hanghang Tong

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AI总结：

针对大语言模型易产生幻觉、知识图谱稀疏时问答准确率下降的问题，提出融合大语言模型与知识图谱推理的LGOT方法，拆解复杂逻辑查询为子问题并分步求解聚合，较ChatGPT性能最高提升20%。

摘要：

尽管大语言模型（LLMs）在诸多任务中表现出色，但在面对对知识准确性有要求的任务时，存在产生幻觉甚至给出错误答案的风险。这一问题在处理需要多步逻辑推理的逻辑查询时尤为突出。另一方面，基于知识图谱（KG）的问答方法能够借助知识图谱准确识别正确答案，但当知识图谱本身稀疏且不完整时，其准确性会迅速下降。如何将知识图谱推理与大语言模型以互利的方式整合，从而同时缓解大语言模型的幻觉问题和知识图谱的不完整性问题，仍是一项关键挑战。本文提出了“Logic-Query-of-Thoughts”（LGOT，思维逻辑查询），这是首个将大语言模型与基于知识图谱的逻辑查询推理相结合的方法。LGOT将知识图谱推理与大语言模型无缝结合，可有效将复杂逻辑查询拆解为易于回答的子问题。通过同时利用知识图谱推理和大语言模型，该方法成功推导出每个子问题的答案。通过聚合这些结果，并为每一步筛选出质量最高的候选答案，LGOT实现了对复杂问题的准确解答。我们的实验结果显示该方法带来了显著的性能提升，相较于ChatGPT最高提升了20%。

英文摘要：

Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand the accuracy of knowledge. The issue becomes even more noticeable when addressing logic queries that require multiple logic reasoning steps. On the other hand, knowledge graph (KG) based question answering methods are capable of accurately identifying the correct answers with the help of knowledge graph, yet its accuracy could quickly deteriorate when the knowledge graph itself is sparse and incomplete. It remains a critical challenge on how to integrate knowledge graph reasoning with LLMs in a mutually beneficial way so as to mitigate both the hallucination problem of LLMs as well as the incompleteness issue of knowledge graphs. In this paper, we propose 'Logic-Query-of-Thoughts' (LGOT) which is the first of its kind to combine LLMs with knowledge graph based logic query reasoning. LGOT seamlessly combines knowledge graph reasoning and LLMs, effectively breaking down complex logic queries into easy to answer subquestions. Through the utilization of both knowledge graph reasoning and LLMs, it successfully derives answers for each subquestion. By aggregating these results and selecting the highest quality candidate answers for each step, LGOT achieves accurate results to complex questions. Our experimental findings demonstrate substantial performance enhancements, with up to 20% improvement over ChatGPT.

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