Explanation Bottleneck Models
This addresses the problem of restricted application in interpretable AI due to limited pre-defined concepts, offering a novel approach for generating natural language explanations, though it is incremental as it builds on existing vision-language models.
The paper tackles the limitation of concept-based interpretable models that rely on pre-defined concept sets by proposing explanation bottleneck models (XBMs), which generate text explanations from inputs without pre-defined concepts and use them for task predictions, achieving accurate and fluent explanations as confirmed in experiments.
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/.