A Multiscale Visualization of Attention in the Transformer Model

Jesse Vig

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arXiv · 1906.05714

A Multiscale Visualization of Attention in the Transformer Model

Jesse Vig

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Abstract

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.

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BibTeX RIS

Jesse Vig. 2019-06-12. A Multiscale Visualization of Attention in the Transformer Model. https://arxiv.org/abs/1906.05714

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