UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
Wilder, Joe, Kadapala, Nikhil, Xu, Benji, Alsaadi, Mohammed, Parsons, Aiden, Rogers, Mitchell, Agarwal, Palash, Hassick, Adam, Dietz, Laura
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Sep-9-2025
- Country:
- Asia
- Afghanistan > Kabul Province
- Kabul (0.04)
- Pakistan (0.14)
- Russia (0.04)
- Afghanistan > Kabul Province
- Europe
- North America > United States
- New Hampshire (0.04)
- North Carolina (0.04)
- Asia
- Genre:
- Research Report (0.83)
- Industry:
- Technology: