UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction

September 08, 2025 ยท Declared Dead ยท ๐Ÿ› Conference and Labs of the Evaluation Forum

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Authors Joe Wilder, Nikhil Kadapala, Benji Xu, Mohammed Alsaadi, Aiden Parsons, Mitchell Rogers, Palash Agarwal, Adam Hassick, Laura Dietz arXiv ID 2509.06883 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.IR Citations 0 Venue Conference and Labs of the Evaluation Forum Last Checked 6 months ago
Abstract
We participate in CheckThat! Task 2 English and explore various methods of prompting and in-context learning, including few-shot prompting and fine-tuning with different LLM families, with the goal of extracting check-worthy claims from social media passages. Our best METEOR score is achieved by fine-tuning a FLAN-T5 model. However, we observe that higher-quality claims can sometimes be extracted using other methods, even when their METEOR scores are lower.
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