Application and Optimization of Large Models Based on Prompt Tuning for Fact-Check-Worthiness Estimation

Application and Optimization of Large Models Based on Prompt Tuning for Fact-Check-Worthiness Estimation Yinglong Y u 1, Hao Shen 2 Zhengyi Lyu 3 and Qi He 4 Communication University of China, Beijing, China 1 yuyingling@cuc.edu.cn 2 shenhao@cuc.edu.cn 3 lyuzhengyi@cuc.edu.cn 4 heqi654321@126.com Abstract --In response to the growing problem of misinformation in the context of globalization and informatization, this paper proposes a classification method for fact-check-worthiness estimation based on prompt tuning. We construct a model for fact-check-worthiness estimation at the methodological level using prompt tuning. By applying designed prompt templates to large language models, we establish in-context learning and leverage prompt tuning technology to improve the accuracy of determining whether claims have fact-check-worthiness, particularly when dealing with limited or unlabeled data. Through extensive experiments on public datasets, we demonstrate that the proposed method surpasses or matches multiple baseline methods in the classification task of fact-check-worthiness estimation assessment, including classical pre-trained models such as BERT, as well as recent popular large models like GPT - 3.5 and GPT -4. Experiments show that the prompt tuning-based method proposed in this study exhibits certain advantages in evaluation metrics such as F1 score and accuracy, thereby effectively validating its effectiveness and advancement in the task of fact-check-worthiness estimation. I NTRODUCTION In today's interconnected world characterized by globalization and informatization, the complexity of multilingual environments and the challenges posed by misinformation have become increasingly severe. With the deepening of international exchanges and the expanding influence of social media, rumors and false information spread rapidly across cyberspace, exacerbating the uncertainty in the global public discourse.