@inproceedings{iarosh-etal-2025-reducing,
    title = "On Reducing Factual Hallucinations in Graph-to-Text Generation Using Large Language Models",
    author = "Iarosh, Dmitrii  and
      Panchenko, Alexander  and
      Salnikov, Mikhail",
    editor = "Gesese, Genet Asefa  and
      Sack, Harald  and
      Paulheim, Heiko  and
      Merono-Penuela, Albert  and
      Chen, Lihu",
    booktitle = "Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.genaik-1.5/",
    pages = "43--53",
    abstract = "Recent work in Graph-to-Text generation has achieved impressive results, but it still suffers from hallucinations in some cases, despite extensive pretraining stages and various methods for working with graph data. While the commonly used metrics for evaluating the quality of Graph-to-Text models show almost perfect results, it makes it challenging to compare different approaches. This paper demonstrates the challenges of recent Graph-to-Text systems in terms of hallucinations and proposes a simple yet effective approach to using a general LLM, which has shown state-of-the-art results and reduced the number of factual hallucinations. We provide step-by-step instructions on how to develop prompts for language models and a detailed analysis of potential factual errors in the generated text."
}