@inproceedings{guo-etal-2025-profiler,
    title = "Profiler: Black-box {AI}-generated Text Origin Detection via Context-aware Inference Pattern Analysis",
    author = "Guo, Hanxi  and
      Cheng, Siyuan  and
      Jin, Xiaolong  and
      Zhang, Zhuo  and
      Shen, Guangyu  and
      Zhang, Kaiyuan  and
      An, Shengwei  and
      Tao, Guanhong  and
      Zhang, Xiangyu",
    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.1265/",
    pages = "24903--24923",
    ISBN = "979-8-89176-332-6",
    abstract = "With the increasing capabilities of Large Language Models (LLMs), the proliferation of AI-generated texts has become a serious concern. Given the diverse range of organizations providing LLMs, it is crucial for governments and third-party entities to identify the origin LLM of a given AI-generated text to enable accurate mitigation of potential misuse and infringement. However, existing detection methods, primarily designed to distinguish between human-generated and LLM-generated texts, often fail to accurately identify the origin LLM due to the high similarity of AI-generated texts from different LLMs. In this paper, we propose a novel black-box AI-generated text origin detection method, dubbed Profiler, which accurately predicts the origin of an input text by extracting distinct context inference patterns through calculating and analyzing novel context losses between the surrogate model{'}s output logits and the adjacent input context. Extensive experimental results show that Profiler outperforms 10 state-of-the-art baselines, achieving more than a 25{\%} increase in AUC score on average across both natural language and code datasets when evaluated against five of the latest commercial LLMs under both in-distribution and out-of-distribution settings."
}