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.
Abstract (translated)
文本概念瓶颈模型（TBMs）是一种设计上可解释的文本分类模型，它们在做出最终预测之前会先预测一组显著的概念。本文提出了一种全新的TBMs生成器——Complete Textual Concept Bottleneck Model (CT-CBM)，该模型完全采用无监督的方式使用小型语言模型来构建概念标签，从而消除了对预定义的人工标注概念和大型语言模型注释的需求。CT-CBM通过在瓶颈层迭代地添加重要的概念来创建一个完整的概念基础，并且通过并行残差连接解决了下游分类泄漏的问题。实验结果表明，CT-CBM在与竞争对手的比较中表现良好，为提高NLP分类器的可解释性而不牺牲性能提供了有希望的解决方案。
URL
https://arxiv.org/abs/2502.11100
