让大模型用自然语言生成并解释图数据库查询，用户可交互修正，提升准确性和可解释性。
A Human-in-the-Loop, LLM-Centered Architecture for Knowledge-Graph Question Answering
- 大模型自动生成可解释的Cypher查询语句，用户用自然语言迭代优化。
- 在合成电影知识图谱上，90个查询的解释质量与错误检测能力显著提升。
- 适合需要高精度、可解释问答的科研与医疗数据应用者。
大型语言模型（LLMs）虽擅长语言理解，但在知识密集型任务中受限于幻觉、信息过时及可解释性差。基于文本的检索增强生成（RAG）虽能将输出锚定在外部来源，却难以处理多跳推理。知识图谱（KGs）支持精确、可解释的查询，但需掌握查询语言。本文提出一种人机协同框架：由大模型生成并解释Cypher图查询，用户通过自然语言逐步优化。该框架应用于真实知识图谱，既提升了复杂数据集的可访问性，又保持事实准确性与语义严谨性，并揭示了模型在不同领域表现差异。核心量化评估为在合成电影知识图谱上的90个查询基准测试，衡量查询解释质量与故障检测能力，辅以两个小型真实场景实验——针对Hyena KG和MaRDI（数学研究数据倡议）KG的查询生成任务。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel at language understanding but remain limited in knowledge-intensive domains due to hallucinations, outdated information, and limited explainability. Text-based retrieval-augmented generation (RAG) helps ground model outputs in external sources but struggles with multi-hop reasoning. Knowledge Graphs (KGs), in contrast, support precise, explainable querying, yet require a knowledge of query languages. This work introduces an interactive framework in which LLMs generate and explain Cypher graph queries and users iteratively refine them through natural language. Applied to real-world KGs, the framework improves accessibility to complex datasets while preserving factual accuracy and semantic rigor and provides insight into how model performance varies across domains. Our core quantitative evaluation is a 90-query benchmark on a synthetic movie KG that measures query explanation quality and fault detection across multiple LLMs, complemented by two smaller real-life query-generation experiments on a Hyena KG and the MaRDI (Mathematical Research Data Initiative) KG.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。