UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
September 08, 2025 ยท Declared Dead ยท ๐ 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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