Application and Optimization of Large Models Based on Prompt Tuning for Fact-Check-Worthiness Estimation
It addresses misinformation detection for fact-checking systems, but appears incremental as it applies existing prompt tuning techniques to a specific task.
This paper tackles the problem of misinformation by proposing a prompt tuning-based classification method for fact-check-worthiness estimation, demonstrating through experiments on public datasets that it surpasses or matches baseline methods like BERT, GPT-3.5, and GPT-4 in metrics such as F1 score and accuracy.
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.