@inproceedings{kletz-etal-2025-better,
    title = "Better Together: Towards Localizing Fact-Related Hallucinations using Open Small Language Models",
    author = "Kletz, David  and
      Mitrovic, Sandra  and
      Dolamic, Ljiljana  and
      Rinaldi, Fabio",
    editor = {Sinha, Aman  and
      V{\'a}zquez, Ra{\'u}l  and
      Mickus, Timothee  and
      Agarwal, Rohit  and
      Buhnila, Ioana  and
      Schmidtov{\'a}, Patr{\'i}cia  and
      Gamba, Federica  and
      Prasad, Dilip K.  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 1st Workshop on Confabulation, Hallucinations and Overgeneration in Multilingual and Practical Settings (CHOMPS 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.chomps-main.3/",
    pages = "20--34",
    ISBN = "979-8-89176-308-1",
    abstract = "In this paper, we explore the potential of Open-source Small Language Models (OSLMs) for localizing hallucinations related to factual accuracy. We first present Lucifer, a dataset designed to enable proper and consistent evaluation of LMs, composed of an automatically constructed portion and a manually curated subset intended for qualitative analysis.We then assess the performance of five OSLMs using four carefully designed prompts. Results are evaluated either individually or merged through a voting-based merging approach. While our results demonstrate that the merging method yields promising performance even with smaller models, our manually curated dataset highlights the inherent difficulty of the task, underscoring the need for further research."
}