@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T GL-CLiC: Global-Local Coherence and Lexical Complexity for Sentence-Level AI-Generated Text Detection
%A Adi, Rizky
%A Irnawan, Bassamtiano Renaufalgi
%A Suzuki, Yoshimi
%A Fukumoto, Fumiyo
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S 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
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F adi-etal-2025-gl
%X 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.
%U https://aclanthology.org/2025.ijcnlp-long.188/
%P 3600-3617
Markdown (Informal)
[GL-CLiC: Global-Local Coherence and Lexical Complexity for Sentence-Level AI-Generated Text Detection](https://aclanthology.org/2025.ijcnlp-long.188/) (Adi et al., IJCNLP-AACL 2025)
ACL
- Rizky Adi, Bassamtiano Renaufalgi Irnawan, Yoshimi Suzuki, and Fumiyo Fukumoto. 2025. GL-CLiC: Global-Local Coherence and Lexical Complexity for Sentence-Level AI-Generated Text Detection. In 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, pages 3600–3617, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.