@inproceedings{chou-etal-2026-iclad,
    title = "{ICLAD}: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection",
    author = "Chou, Benjamin Shiue-Hal  and
      Zhu, Yi  and
      Koppisetti, Surya",
    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.450/",
    pages = "9242--9256",
    ISBN = "979-8-89176-395-1",
    abstract = "Audio deepfakes pose a significant security threat, yet current state-of-the-art (SOTA) detection systems do not generalize well to realistic in-the-wild deepfakes. We introduce a novel In-Context Learning paradigm with comparison-guidance for Audio Deepfake detection (ICLAD). The framework enables the use of audio language models (ALMs) for training-free generalization to unseen deepfakes and provides rich textual explanations on the detection outcome. At the core of ICLAD is a pairwise comparative reasoning strategy that guides the ALM to discover and filter hallucinations and deepfake-irrelevant acoustic attributes. The ALM works alongside a specialized deepfake detector, whereby a routing mechanism feeds out-of-distribution samples to the ALM. On in-the-wild datasets, ICLAD improves macro F1 over the specialized detector, with up to 2{\texttimes} relative improvement. Further analysis demonstrates the flexibility of ICLAD and its potential for deployment on recent open-source ALMs."
}