Feature Extraction for Claim Check-Worthiness Prediction Tasks Using LLM
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Yuka Teramoto
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Takahiro Komamizu
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Mitsunori Matsushita
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Kenji Hatano
概要
This study explores the use of Large Language Models (LLMs) for Claim Check-Worthiness Prediction (CCWP), a crucial pre-screening task in fact-checking. We predict the time between a claim’s occurrence and verification by analyzing data from fact-checking organizations. The results show that validation time is the same between the top 25% and bottom 75% of total checklist condition fulfillment claims. That is, further optimization is needed for LLMs to perform effective CCWPs.
引用情報
Yuka Teramoto, Takahiro Komamizu, Mitsunori Matsushita, , Kenji Hatano, Feature Extraction for Claim Check-Worthiness Prediction Tasks Using LLM, Information Integration and Web Intelligence, pp.53--58, 2024-12-02, DOI: 10.1007/978-3-031-78090-5_5.