@inproceedings{tan-etal-2025-open,
    title = "Open-World Authorship Attribution",
    author = "Tan, Xinhao  and
      Liu, Songhua  and
      Cong, Xia  and
      Li, Kunjun  and
      Wang, Xinchao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.913/",
    pages = "17744--17758",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent years have witnessed rapid advancements in Large Language Models (LLMs). Nevertheless, it remains unclear whether state-of-the-art LLMs can infer the author of an anonymous research paper solely from the text, without any additional information. To investigate this novel challenge, which we define as Open-World Authorship Attribution, we introduce a benchmark comprising thousands of research papers across various fields to quantitatively assess model capabilities. Then, at the core of this paper, we tailor a two-stage framework to tackle this problem: candidate selection and authorship decision. Specifically, in the first stage, LLMs are prompted to generate multi-level key information, which are then used to identify potential candidates through Internet searches. In the second stage, we introduce key perspectives to guide LLMs in determining the most likely author from these candidates. Extensive experiments on our benchmark demonstrate the effectiveness of the proposed approach, achieving 60.7{\%} and 44.3{\%} accuracy in the two stages, respectively. We will release our benchmark and source codes to facilitate future research in this field."
}