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- W4381247900 abstract "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." @default.
- W4381247900 created "2023-06-20" @default.
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- W4381247900 date "2023-06-15" @default.
- W4381247900 modified "2023-09-26" @default.
- W4381247900 title "Clickbait Detection via Large Language Models" @default.
- W4381247900 doi "https://doi.org/10.48550/arxiv.2306.09597" @default.
- W4381247900 hasPublicationYear "2023" @default.
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