@inproceedings{voznyuk-etal-2025-advacheck,
    title = "Advacheck at {S}em{E}val-2025 Task 3: Combining {NER} and {RAG} to Spot Hallucinations in {LLM} Answers",
    author = "Voznyuk, Anastasia  and
      Gritsai, German  and
      Grabovoy, Andrey",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.160/",
    pages = "1204--1210",
    ISBN = "979-8-89176-273-2",
    abstract = "The Mu-SHROOM competition in the SemEval-2025 Task 3 aims to tackle the problem of detecting spans with hallucinations in texts, generated by Large Language Models (LLMs). Our developed system, submitted to this task, is a joint architecture that utilises Named Entity Recognition (NER), Retrieval-Augmented Generation (RAG) and LLMs to gather, compare and analyse information in the texts provided by organizers. We extract entities potentially capable of containing hallucinations with NER, aggregate relevant topics for them using RAG, then verify and provide a verdict on the extracted information using the LLMs. This approach allowed with a certain level of quality to find hallucinations not only in facts, but misspellings in names and titles, which was not always accepted by human annotators in ground truth markup. We also point out some inconsistencies within annotators spans, that perhaps affected scores of all participants."
}