UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
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Abstract (arXiv)
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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References · 7
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
- 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
- 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
- 2023Large Language Modelscited by 780
- 2021True Few-Shot Learning with Language Modelscited by 344
- 2025Towards Effective Extraction and Evaluation of Factual Claimscited by 19
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