@inproceedings{puccetti-etal-2024-ai,
    title = "{AI} `News' Content Farms Are Easy to Make and Hard to Detect: A Case Study in {I}talian",
    author = "Puccetti, Giovanni  and
      Rogers, Anna  and
      Alzetta, Chiara  and
      Dell{'}Orletta, Felice  and
      Esuli, Andrea",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.acl-long.817/",
    doi = "10.18653/v1/2024.acl-long.817",
    pages = "15312--15338",
    abstract = "Large Language Models (LLMs) are increasingly used as `content farm' models (CFMs), to generate synthetic text that could pass for real news articles. This is already happening even for languages that do not have high-quality monolingual LLMs. We show that fine-tuning Llama (v1), mostly trained on English, on as little as 40K Italian news articles, is sufficient for producing news-like texts that native speakers of Italian struggle to identify as synthetic.We investigate three LLMs and three methods of detecting synthetic texts (log-likelihood, DetectGPT, and supervised classification), finding that they all perform better than human raters, but they are all impractical in the real world (requiring either access to token likelihood information or a large dataset of CFM texts). We also explore the possibility of creating a proxy CFM: an LLM fine-tuned on a similar dataset to one used by the real `content farm'. We find that even a small amount of fine-tuning data suffices for creating a successful detector, but we need to know which base LLM is used, which is a major challenge.Our results suggest that there are currently no practical methods for detecting synthetic news-like texts `in the wild', while generating them is too easy. We highlight the urgency of more NLP research on this problem."
}