@inproceedings{lu-etal-2026-synergizing,
    title = "Synergizing Stylometrics with Semantics: Dual-Path Framework for {LLM} Detection and Attribution",
    author = "Lu, Xingyu  and
      Ma, Yumeng  and
      Zhou, Xiang  and
      Gan, Shengli  and
      Deng, Guiying  and
      Wen, Yang  and
      Liu, Yanbing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1855/",
    pages = "37252--37265",
    ISBN = "979-8-89176-395-1",
    abstract = "The widespread application of LLMs has made MGT detection increasingly important in cyberspace security and governance. The existing detection paradigms mainly focus on statistical likelihood or deep embeddings. However, in complex applications such as short texts, derivative works, and cross-domain content, the discriminative capabilities fragility of these conventional methods increases significantly with the development of LLMs. Conversely, our research reveals that LLMs exhibit inherent style inertia. To address these limitations, this study attempts to synergize stylometrics and semantics for identifying MGT. This approach draws from the forensic perspective of experts who detect human imitation by focusing on stylistic nuances. Based on the above inspiration, we propose Stylometric-Semantic LLM Attribution (SSLA), a framework that extracts model-specific stylistic fingerprints across lexical, syntactic, and structural dimensions. SSLA employs a dual-path attention fusion architecture to dynamically integrate explicit stylistic signals with implicit semantic encodings. Extensive experiments across six LLM families demonstrate that our method achieves state-of-the-art performance. Notably, SSLA achieves a Macro-F1 score of 95.6{\%} on the challenging Wikipedia dataset, demonstrating exceptional robustness and surpassing state-of-the-art baselines like OTBDetector."
}