@inproceedings{haroon-etal-2025-caisa,
    title = "{CAISA} at {S}em{E}val-2025 Task 7: Multilingual and Cross-lingual Fact-Checked Claim Retrieval",
    author = "Haroon, Muqaddas  and
      Ashraf, Shaina  and
      Baris, Ipek  and
      Flek, Lucie",
    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-1.183/",
    pages = "1377--1382",
    ISBN = "979-8-89176-273-2",
    abstract = "We leveraged LLaMA, utilizing its ability to evaluate the relevance of retrieved claims within a retrieval-based fact-checking framework. This approach aimed to explore the impact of large language models (LLMs) on retrieval tasks and assess their effectiveness in enhancing fact-checking accuracy. Additionally, we integrated Jina embeddings v2 and the MPNet multilingual sentence transformer to filter and rank a set of 500 candidate claims. These refined claims were then used as input for LLaMA, ensuring that only the most contextually relevant ones were assessed."
}