@inproceedings{bhan-etal-2025-towards,
    title = "Towards Achieving Concept Completeness for Textual Concept Bottleneck Models",
    author = {Bhan, Milan  and
      Choho, Yann  and
      Vittaut, Jean-No{\"e}l  and
      Chesneau, Nicolas  and
      Moreau, Pierre  and
      Lesot, Marie-Jeanne},
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.106/",
    doi = "10.18653/v1/2025.findings-emnlp.106",
    pages = "2007--2024",
    ISBN = "979-8-89176-335-7",
    abstract = "This paper proposes Complete Textual Concept Bottleneck Model (CT-CBM), a novel TCBM generator building concept labels in a fully unsupervised manner using a small language model, eliminating both the need for predefined human labeled concepts and LLM annotations. CT-CBM iteratively targets and adds important and identifiable concepts in the bottleneck layer to create a complete concept basis. CT-CBM achieves striking results against competitors in terms of concept basis completeness and concept detection accuracy, offering a promising solution to reliably enhance interpretability of NLP classifiers."
}