@inproceedings{chen-etal-2025-preserving,
    title = "Preserving Zero-shot Capability in Supervised Fine-tuning for Multi-label Text Classification",
    author = "Chen, Si-An  and
      Lin, Hsuan-Tien  and
      Lin, Chih-Jen",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-naacl.315/",
    pages = "5699--5712",
    ISBN = "979-8-89176-195-7",
    abstract = "Zero-shot multi-label text classification (ZMTC) requires models to predict multiple labels for a document, including labels unseen during training. Previous work assumes that models leveraging label descriptions ensures zero-shot capability. However, we find that supervised methods, despite achieving strong overall performance, lose their zero-shot capability during training, revealing a trade-off between overall and zero-shot performance. To address the issue, we propose OF-DE and OF-LAN, which preserve the zero-shot capabilities of powerful dual encoder and label-wise attention network architectures by freezing the label encoder. Additionally, we introduce a self-supervised auxiliary loss to further improve zero-shot performance. Experiments demonstrate that our approach significantly improves zero-shot performance of supervised methods while maintaining strong overall accuracy."
}