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 serious 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 scenarios on a number of 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 the human intuition, the experiments demonstrated that LLMs cannot make satisfied clickbait detection just by the headlines.

翻译：点击诱饵，旨在通过令人惊讶甚至耸人听闻的标题诱导用户提高点击率，几乎渗透到所有在线内容发布平台，如新闻门户和社交媒体。近年来，大型语言模型（LLMs）作为一种强大的工具，在一系列自然语言处理下游任务中取得了巨大成功。然而，目前尚不明确LLMs是否能够作为高质量的点击诱饵检测系统。本文分析了LLMs在少量样本场景下对多个英文和中文基准数据集的表现。实验结果表明，相较于最先进的深度方法和微调预训练语言模型，LLMs无法取得最佳结果。与人类直觉不同，实验证明LLMs仅凭标题无法实现令人满意的点击诱饵检测。