Towards Achieving Concept Completeness for Textual Concept Bottleneck Models
Milan Bhan, Yann Choho, Jean-Noël Vittaut, Nicolas Chesneau, Pierre R. Moreau, Marie‐Jeanne Lesot · 2025
Textual Concept Bottleneck Models (TCBMs) are interpretable-by-design models for text classification that predict a set of salient concepts before making the final prediction.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.* These authors contributed equally to this work. C3M CB-LLM CT-CBM(ours) Need for predefined concepts Yes