@inproceedings{lucas-etal-2023-fighting,
    title = "\textbf{Fighting Fire with Fire: The Dual Role of {LLM}s in Crafting and Detecting Elusive Disinformation}",
    author = "\textbf{Lucas, Jason}  and
      Uchendu, Adaku  and
      Yamashita, Michiharu  and
      Lee, Jooyoung  and
      Rohatgi, Shaurya  and
      Lee, Dongwon",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "\textbf{Association for Computational Linguistics}",
    url = "https://aclanthology.org/2023.emnlp-main.883",
    pages = "14279--14305",
    abstract = "Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (*.i.e, generating large-scale harmful and misleading content*). To combat this emerging risk of LLMs, we propose a novel {``}***Fighting Fire with Fire***{''} (F3) strategy that harnesses modern LLMs{'} generative and emergent reasoning capabilities to counter human-written and LLM-generated disinformation. First, we leverage GPT-3.5-turbo to synthesize authentic and deceptive LLM-generated content through paraphrase-based and perturbation-based prefix-style prompts, respectively. Second, we apply zero-shot in-context semantic reasoning techniques with cloze-style prompts to discern genuine from deceptive posts and news articles. In our extensive experiments, we observe GPT-3.5-turbo{'}s zero-shot superiority for both in-distribution and out-of-distribution datasets, where GPT-3.5-turbo consistently achieved accuracy at 68-72{\%}, unlike the decline observed in previous customized and fine-tuned disinformation detectors. Our codebase and dataset are available at https://github.com/mickeymst/F3.",
}