@inproceedings{wagner-etal-2025-mitigating,
    title = "Mitigating Hallucination by Integrating Knowledge Graphs into {LLM} Inference {--} a Systematic Literature Review",
    author = "Wagner, Robin  and
      Kitzelmann, Emanuel  and
      Boersch, Ingo",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-srw.53/",
    pages = "795--805",
    ISBN = "979-8-89176-254-1",
    abstract = "Large Language Models (LLMs) demonstrate strong performance on different language tasks, but tend to hallucinate {--} generate plausible but factually incorrect outputs. Recently, several approaches to integrate Knowledge Graphs (KGs) into LLM inference were published to reduce hallucinations. This paper presents a systematic literature review (SLR) of such approaches. Following established SLR methodology, we identified relevant work by systematically search in different academic online libraries and applying a selection process. Nine publications were chosen for in-depth analysis. Our synthesis reveals differences and similarities of how the KG is accessed, traversed, and how the context is finally assembled. KG integration can significantly improve LLM performance on benchmark datasets and additionally to mitigate hallucination enhance reasoning capabilities, explainability, and access to domain-specific knowledge. We also point out current limitations and outline directions for future work."
}