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complex QA tasks due to poor reasoning capacity, outdated knowledge, and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complex QA tasks due to poor reasoning capacity, outdated knowledge, and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