Explanation Bottleneck Models

TL;DR

This paper introduces Explanation Bottleneck Models (XBMs), which generate natural language explanations for vision tasks without relying on pre-defined concepts, improving interpretability and flexibility.

Contribution

XBMs are a novel neural network architecture that produce text explanations directly from input, bypassing the need for pre-defined concept sets, and leverage pre-trained vision-language models.

Findings

XBMs achieve accurate natural language explanations.

XBMs outperform concept bottleneck models in explanation quality.

Code is publicly available for reproducibility.

Abstract

Recent concept-based interpretable models have succeeded in providing meaningful explanations by pre-defined concept sets. However, the dependency on the pre-defined concepts restricts the application because of the limited number of concepts for explanations. This paper proposes a novel interpretable deep neural network called explanation bottleneck models (XBMs). XBMs generate a text explanation from the input without pre-defined concepts and then predict a final task prediction based on the generated explanation by leveraging pre-trained vision-language encoder-decoder models. To achieve both the target task performance and the explanation quality, we train XBMs through the target task loss with the regularization penalizing the explanation decoder via the distillation from the frozen pre-trained decoder. Our experiments, including a comparison to state-of-the-art concept bottleneck models, confirm that XBMs provide accurate and fluent natural language explanations without pre-defined concept sets. Code is available at https://github.com/yshinya6/xbm/.