Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers

TL;DR

This paper introduces a universal explainability method for Transformer-based models, including multi-modal and encoder-decoder architectures, improving interpretability over existing single-modality approaches.

Contribution

It presents the first generic explanation technique applicable to various Transformer architectures, including bi-modal and co-attention models.

Findings

Outperforms existing explainability methods for Transformers.

Effective across self-attention, co-attention, and encoder-decoder models.

Enhances interpretability in multi-modal reasoning tasks.

Abstract

Transformers are increasingly dominating multi-modal reasoning tasks, such as visual question answering, achieving state-of-the-art results thanks to their ability to contextualize information using the self-attention and co-attention mechanisms. These attention modules also play a role in other computer vision tasks including object detection and image segmentation. Unlike Transformers that only use self-attention, Transformers with co-attention require to consider multiple attention maps in parallel in order to highlight the information that is relevant to the prediction in the model's input. In this work, we propose the first method to explain prediction by any Transformer-based architecture, including bi-modal Transformers and Transformers with co-attentions. We provide generic solutions and apply these to the three most commonly used of these architectures: (i) pure self-attention, (ii) self-attention combined with co-attention, and (iii) encoder-decoder attention. We show that our method is superior to all existing methods which are adapted from single modality explainability.