@inproceedings{rathwa-etal-2025-agi,
    title = "``{AGI}'' team at {SHROOM}-{CAP}: Data-Centric Approach to Multilingual Hallucination Detection using {XLM}-{R}o{BERT}a",
    author = "Rathwa, Harsh  and
      Mishra, Pruthwik  and
      Malviya, Shrikant",
    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.10/",
    pages = "96--100",
    ISBN = "979-8-89176-308-1",
    abstract = "The detection of hallucinations in multilingual scientific text generated by Large Language Models (LLMs) presents significant challenges for reliable AI systems. This paper describes our submission to the SHROOM-CAP 2025 shared task on scientific hallucination detection across 9 languages. Unlike most approaches that focus primarily on model architecture, we adopted a data-centric strategy that addressed the critical issue of training data scarcity and imbalance. We unify and balance five existing datasets to create a comprehensive training corpus of 124,821 samples (50{\%} correct, 50{\%} hallucinated), representing a 172x increase over the original SHROOM training data. Our approach fine-tuned XLM-RoBERTa-Large with 560 million parameters on this enhanced dataset, achieves competitive performance across all languages, including \textbf{2nd place in Gujarati} (zero-shot language) with Factuality F1 of 0.5107, and rankings between 4th-6th place across the remaining 8 languages. Our results demonstrate that systematic data curation can significantly outperform architectural innovations alone, particularly for low-resource languages in zero-shot settings."
}