Large Language Models Meet Knowledge Graphs for Question Answering:
  Synthesis and Opportunities
Authors: name: Chuangtao Ma, name: Yongrui Chen, name: Tianxing Wu, name: Arijit Khan, name: Haofen Wang

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complex QA tasks due to poor reasoning capacity, outdated knowledge, and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.