title: Large Language Models Meet Knowledge Graphs for Question Answering:
    Synthesis and Opportunities
summary: 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  complex QA tasks due to poor reasoning capacity, outdated knowledge, and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 advances 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.
paper_authors._id: 6839b771cdef5cabcc651a24
paper_authors.name: Chuangtao Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:45:18.037Z
paper_authors._id: 66c8b6e42400073af3219888
paper_authors.avatarUrl: /avatars/9b37a5a6e20d5389d53686663b9ef7a1.svg
paper_authors.fullname: Chuangtao Ma
paper_authors.type: user
paper_authors.user: ctma
paper_authors._id: 6839b771cdef5cabcc651a25
paper_authors.name: Yongrui Chen
paper_authors._id: 6839b771cdef5cabcc651a26
paper_authors.name: Tianxing Wu
paper_authors._id: 6839b771cdef5cabcc651a27
paper_authors.name: Arijit Khan
paper_authors._id: 6839b771cdef5cabcc651a28
paper_authors.name: Haofen Wang
paper_title: Large Language Models Meet Knowledge Graphs for Question Answering:
    Synthesis and Opportunities
paper_summary: 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  complex QA tasks due to poor reasoning capacity, outdated knowledge, and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 advances 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.
paper_ai_summary: This survey categorizes and analyzes methods for integrating large language models with knowledge graphs to improve their performance on complex question-answering tasks.
paper_ai_keywords: large language models knowledge graphs natural language understanding generation reasoning capacity hallucinations systematization state-of-the-art evaluation metrics benchmark datasets