Towards Achieving Concept Completeness for Unsupervised Textual Concept Bottleneck Models

• URL: http://arxiv.org/abs/2502.11100v1
• Date: Sun, 16 Feb 2025 12:28:43 GMT
• Title: Towards Achieving Concept Completeness for Unsupervised Textual Concept Bottleneck Models
• Authors: Milan Bhan, Yann Choho, Pierre Moreau, Jean-Noel Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot,
• Abstract summary: Textual Concept Bottleneck Models (TBMs) 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.
• Score: 0.3694429692322631
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• Abstract: Textual Concept Bottleneck Models (TBMs) 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 concepts in the bottleneck layer to create a complete concept basis and addresses downstream classification leakage through a parallel residual connection. CT-CBM achieves good results against competitors, offering a promising solution to enhance interpretability of NLP classifiers without sacrificing performance.

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