@inproceedings{pal-etal-2025-indicclaimbuster,
    title = "{I}ndic{C}laim{B}uster: A Multilingual Claim Verification Dataset",
    author = "Pal, Pritam  and
      Jana, Shyamal Krishna  and
      Das, Dipankar",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.133/",
    pages = "2478--2489",
    ISBN = "979-8-89176-298-5",
    abstract = "The present article introduces **IndicClaimBuster**, a novel multilingual claim verification dataset comprising $\approx$ 9K claims and their corresponding evidence in English, Hindi, Bengali, and Hindi-English CodeMixed texts. The data set covers three key domains: politics, law and order, and health, to address the challenges of verifiable facts. Each claim was sourced from reputable Indian news portals and is accompanied by three pieces of evidence, two LLM-generated and one manually curated. Additionally, a separate attempt was conducted to generate refuted claims by employing an LLM. We further develop two frameworks: an unsupervised baseline and a two-stage pipeline that comprises evidence retrieval and veracity prediction modules. For retrieval, we fine-tuned SBERT models, with e5-base demonstrating superior average performance across languages, whereas for veracity prediction, multilingual transformers (mBERT, XLM-R, MuRIL, IndicBERTv2) were fine-tuned. Results indicate MuRIL and IndicBERTv2 excel in Indian languages, while XLM-R performs the best for CodeMix. Our work contributes a high-quality multilingual dataset and strong baseline methodologies, offering valuable resources for advancing automated claim verification in linguistically diverse and low-resource settings for Indian languages. The IndicClaimBuster dataset is available at: https://github.com/pritampal98/indic-claim-buster"
}