融合大模型与知识图谱，提升复杂问答的准确性和可靠性。
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
- 按问答类型与知识图谱角色构建新分类体系
- 系统对比主流方法在推理、知识时效性上的表现
- 适合关注智能问答系统优化的研究者与开发者
大语言模型（LLMs）凭借强大的自然语言理解与生成能力，在问答任务中表现出色。然而，面对复杂问答时，其推理能力不足、知识过时及幻觉问题仍制约性能。近期研究尝试将大模型与知识图谱（KG）融合以解决上述挑战。本文提出一种新的结构化分类体系，根据问答类型和知识图谱在集成中的角色对方法进行分类。系统梳理了当前最先进的大模型与知识图谱融合问答技术，从优势、局限性及知识图谱需求角度进行比较分析。进一步将各类方法与不同复杂问答场景对齐，探讨其应对核心挑战的机制。最后总结进展、评估指标与基准数据集，指出开放问题与未来机遇。
原文摘要 · Abstract (English)
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 However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations.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.
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