@inproceedings{gupta-li-2025-seeing,
    title = "Seeing Through the Mask: {AI}-Generated Text Detection with Similarity-Guided Graph Reasoning",
    author = "Gupta, Nidhi  and
      Li, Qinghua",
    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.findings-ijcnlp.84/",
    pages = "1350--1360",
    ISBN = "979-8-89176-303-6",
    abstract = "The rise of generative AI has led to challenges in distinguishing AI-generated text from human-written content, raising concerns about misinformation and content authenticity. Detecting AI-generated text remains challenging, especially under various stylistic domains and paraphrased inputs. We introduce SGG-ATD, a novel detection framework that models structural and contextual relationships between LLM-predicted and original-input text. By masking parts of the input and reconstructing them using a language model, we capture implicit coherence patterns. These are encoded in a graph where cosine and contextual links between keywords guide classification via a Graph Convolutional Network (GCN). SGG-ATD achieves strong performance across diverse datasets and shows resilience to adversarial rephrasing and out-of-distribution inputs, outperforming competitive baselines."
}