Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media. Recently, Large Language Models (LLMs) have emerged as a powerful instrument and achieved tremendous success in a series of NLP downstream tasks. However, it is not yet known whether LLMs can be served as a high-quality clickbait detection system. In this paper, we analyze the performance of LLMs in the few-shot and zero-shot scenarios on several English and Chinese benchmark datasets. Experimental results show that LLMs cannot achieve the best results compared to the state-of-the-art deep and fine-tuning PLMs methods. Different from human intuition, the experiments demonstrated that LLMs cannot make satisfied clickbait detection just by the headlines.
View on arXiv@article{wang2025_2306.09597,
title={ Clickbait Detection via Large Language Models },
author={ Han Wang and Yi Zhu and Ye Wang and Yun Li and Yunhao Yuan and Jipeng Qiang },
journal={arXiv preprint arXiv:2306.09597},
year={ 2025 }
}