@inproceedings{chen-etal-2025-divscore,
    title = "{D}iv{S}core: Zero-Shot Detection of {LLM}-Generated Text in Specialized Domains",
    author = "Chen, Zhihui  and
      He, Kai  and
      Huang, Yucheng  and
      Zhu, Yunxiao  and
      Feng, Mengling",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.971/",
    pages = "19242--19264",
    ISBN = "979-8-89176-332-6",
    abstract = "Detecting LLM-generated text in specialized and high-stakes domains like medicine and law is crucial for combating misinformation and ensuring authenticity. However, current zero-shot detectors, while effective on general text, often fail when applied to specialized content due to domain shift. We provide a theoretical analysis showing this failure is fundamentally linked to the KL divergence between human, detector, and source text distributions. To address this, we propose DivScore, a zero-shot detection framework using normalized entropy-based scoring and domain knowledge distillation to robustly identify LLM-generated text in specialized domains. Experiments on medical and legal datasets show that DivScore consistently outperforms state-of-the-art detectors, with 14.4{\%} higher AUROC and 64.0{\%} higher recall at 0.1{\%} false positive rate threshold. In adversarial settings, DivScore demonstrates superior robustness to other baselines, achieving on average 22.8{\%} advantage in AUROC and 29.5{\%} in recall."
}