@inproceedings{adi-etal-2025-gl,
    title = "{GL}-{CL}i{C}: Global-Local Coherence and Lexical Complexity for Sentence-Level {AI}-Generated Text Detection",
    author = "Adi, Rizky  and
      Irnawan, Bassamtiano Renaufalgi  and
      Suzuki, Yoshimi  and
      Fukumoto, Fumiyo",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.188/",
    pages = "3600--3617",
    ISBN = "979-8-89176-298-5",
    abstract = "Unlike document-level AI-generated text (AIGT) detection, sentence-level AIGT detection remains underexplored, despite its importance for addressing collaborative writing scenarios where humans modify AIGT suggestions on a sentence-by-sentence basis. Prior sentence-level detectors often neglect the valuable context surrounding the target sentence, which may contain crucial linguistic artifacts that indicate a potential change in authorship. We propose **GL-CLiC**, a novel technique that leverages both **G**lobal and **L**ocal signals of **C**oherence and **L**ex**i**cal **C**omplexity, which we operationalize through discourse analysis and CEFR-based vocabulary sophistication. **GL-CLiC** models local coherence and lexical complexity by examining a sentence{'}s relationship with its neighbors or peers, complemented with its document-wide analysis. Our experimental results show that **GL-CLiC** achieves superior performance and better generalization across domains compared to existing methods."
}