@inproceedings{nguyen-etal-2025-leveraging-text,
    title = "Leveraging Text-to-Text Transformers as Classifier Chain for Few-Shot Multi-Label Classification",
    author = "Nguyen, Quang Anh  and
      Tomeh, Nadi  and
      Lebbah, Mustapha  and
      Charnois, Thierry  and
      Azzag, Hanane",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1368/",
    pages = "26929--26938",
    ISBN = "979-8-89176-332-6",
    abstract = "Multilabel text classification (MLTC) is an essential task in NLP applications. Traditional methods require extensive labeled data and are limited to fixed label sets. Extracting labels by LLMs is more effective and universal, but incurs high computational costs. In this work, we introduce a distillation-based T5 generalist model for zero-shot MLTC and few-shot fine-tuning. Our model accommodates variable label sets with general domain-agnostic pertaining, while modeling dependency between labels. Experiments show that our approach outperforms baselines of similar size on three few-shot tasks.Our code is available at https://anonymous.4open.science/r/t5-multilabel-0C32/README.md"
}