@inproceedings{tong-etal-2025-generate,
    title = "Generate First, Then Sample: Enhancing Fake News Detection with {LLM}-Augmented Reinforced Sampling",
    author = "Tong, Zhao  and
      Gu, Yimeng  and
      Liu, Huidong  and
      Liu, Qiang  and
      Wu, Shu  and
      Shi, Haichao  and
      Zhang, Xiao-Yu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1182/",
    pages = "24276--24290",
    ISBN = "979-8-89176-251-0",
    abstract = "The spread of fake news on online platforms has long been a pressing concern. Considering this, extensive efforts have been made to develop fake news detectors. However, a major drawback of these models is their relatively low performance{---}lagging by more than 20{\%}{---}in identifying *fake* news compared to *real* news, making them less suitable for practical deployment. This gap is likely due to an imbalance in the dataset and the model{'}s inadequate understanding of data distribution on the targeted platform. In this work, we focus on improving the model{'}s effectiveness in detecting *fake* news. To achieve this, we **first** adopt an LLM to **generate** fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news. **Then**, we apply Reinforcement Learning to dynamically **sample** fake news, allowing the model to learn the optimal real-to-fake news ratio for training an effective fake news detector on the targeted platform. This approach allows our model to perform effectively even with a limited amount of annotated news data and consistently improve detection accuracy across different platforms. Experimental results demonstrate that our approach achieves state-of-the-art performance on two benchmark datasets, improving *fake* news detection performance by 24.02{\%} and 11.06{\%} respectively."
}