Towards Achieving Concept Completeness for Textual Concept Bottleneck Models

• URL: http://arxiv.org/abs/2502.11100v3
• Date: Wed, 28 May 2025 08:49:18 GMT
• Title: Towards Achieving Concept Completeness for Textual Concept Bottleneck Models
• Authors: Milan Bhan, Yann Choho, Pierre Moreau, Jean-Noel Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot,
• Abstract summary: This paper proposes a novel TCBM generator building concept labels in a fully unsupervised manner using a small language model.<n>CT-CBM iteratively targets and adds important and identifiable concepts in the bottleneck layer to create a complete concept basis.<n>CT-CBM achieves striking results against competitors in terms of concept basis and concept detection accuracy.
• Score: 0.3694429692322631
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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