Caption Bottleneck Models
Caption Bottleneck Models (CaBM) replaces rigid concept layers with free-form natural language by using Large Multimodal Model (LMM) generated captions as input for a text classifier. This method avoids predefined concept sets and information leakage by construction, allowing the model to autonomously discover high-quality, dataset-specific concepts from the text alone.