@inproceedings{devadiga-etal-2025-tifin,
    title = "{TIFIN} {I}ndia at {S}em{E}val-2025: Harnessing Translation to Overcome Multilingual {IR} Challenges in Fact-Checked Claim Retrieval",
    author = "Devadiga, Prasanna  and
      Suneesh, Arya  and
      Rajpoot, Pawan  and
      Hazarika, Bharatdeep  and
      Baliga, Aditya",
    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-1.299/",
    pages = "2297--2304",
    ISBN = "979-8-89176-273-2",
    abstract = "We address the challenge of retrieving previously fact-checked claims in mono-lingual and cross-lingual settings - a critical task given the global prevalence of disinformation. Our approach follows a two-stage strategy: a reliable baseline retrieval system using a fine-tuned embedding model and an LLM-based reranker. Our key contribution is demonstrating how LLM-based translation can overcome the hurdles of multilingual information retrieval. Additionally, we focus on ensuring that the bulk of the pipeline can be replicated on a consumer GPU. Our final integrated system achieved a success@10 score of 0.938 ({\textasciitilde}0.94) and 0.81025 on the monolingual and crosslingual test sets respectively."
}