A Multiscale Visualization of Attention in the Transformer Model
The Transformer is a sequence model that forgoes traditional recurrent architectures in favor of a fully attention based approach. Besides improving performance, an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements. However, the multi layer, multi head attention mechanism in the Transformer model can be difficult to decipher. To make the model more accessible, we introduce an open source tool that visualizes attention at multiple scales, each of which provides a unique perspective on the attention mechanism. We demonstrate the tool on BERT and OpenAI GPT 2 and present three example use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior.
ACL