@inproceedings{xiong-etal-2026-verifiable,
    title = "Verifiable {LLM}-Generated Text Detection via Projected Semantic-Structural Distributions",
    author = "Xiong, Ruochong  and
      Li, Qien  and
      Lian, Wangwang  and
      Wan, Yulong  and
      Xue, Hanlin  and
      Tan, Zhouxing  and
      Yang, Han  and
      Lu, Fengyu  and
      Liu, Junfei",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.638/",
    pages = "14005--14042",
    ISBN = "979-8-89176-390-6",
    abstract = "The widespread deployment of large language models (LLMs) makes detecting LLM-Generated text a critical security task. Existing methods, primarily relying on output probabilities from proxy models or single semantic features, suffer from distribution misalignment and limited interpretability. We observe that machine-generated text exhibits a directionally consistent systematic translation relative to human-written text within the joint semantic-structural space. Accordingly, we propose ProSSD, a statistical framework utilizing supervised subspace learning to extract compact features and construct conditional semantic distributions based on syntactic structures. By employing a likelihood ratio test, we derive a modified Mahalanobis distance, weighted by the Wasserstein distance, as the discriminative metric. Experiments demonstrate ProSSD{'}s superior robustness and computational efficiency across cross-domain, cross-model, and adversarial scenarios. Furthermore, we reveal the phenomena of systematic semantic translation and semantic collapse in machine-generated text, offering interpretable statistical insights into LLM generation behaviors."
}