Source-linked AI summary
A Multiscale Visualization of Attention in the Transformer Model
Jesse Vig
TL;DR
The Transformer’s multi-layer, multi-head attention is difficult to decipher, despite attention offering a way to inspect how models weight input elements. This paper introduces an open-source tool with multiple visualization scales and demonstrates it on BERT and GPT-2. The tool supports use cases involving model bias, relevant attention heads, and links between neurons and model behavior.
Problem
Transformer attention can help interpret models through input-weight visualization, but its multi-layer, multi-head structure is difficult to decipher.
Method
The paper introduces an open-source multiscale visualization tool with attention-head, model, and neuron views for BERT and GPT-2.
Results
The tool is demonstrated in three use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior.
Takeaways & Limitations
The visualizations provide perspectives for examining attention patterns, potential bias, task-relevant heads, and neuron contributions to attention behavior.
Takeaways & Limitations
Neuron-view values are specific to a particular layer and attention head.
Abstract
from arXiv · showhide
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.
1 Introduction
Transformers replace recurrent sequence modeling with fully attention-based processing, offering interpretability through input-weight visualization but creating a difficult mult layer, multi-head analysis problem. The paper addresses this challenge with a multiscale visualization tool for Transformer attention.
- Transformers use fully attention-based processing instead of traditional recurrent architectures.
- Attention can aid interpretation by showing how a model assigns weight to different input elements.Its value for explaining individual predictions may nevertheless be limited.
- Existing NLP attention visualizations include attention-matrix heatmaps and bipartite graph representations.
- BERT-Large produces 24 × 16 = 384 unique attention structures per input because attention varies across 24 layers and 16 heads.This multi-layer, multi-head mechanism makes Transformer attention difficult to visualize and decipher.
- The paper introduces multiscale visualization with high-level model and low-level neuron views, adapting the tool to decoder-only GPT-2 and encoder-only BERT.
2 Visualization Tool
The tool visualizes Transformer attention at multiple scales, from individual heads and whole-model patterns to neuron-level computations. Demonstrations on GPT-2 and BERT show distinct positional, lexical, sentence-pair, bias-related, and neuron-level patterns, supporting model exploration and interpretation.
- Visualization Tool: The open-source tool provides attention-head, model, and neuron views for GPT-2 and BERT.The views offer complementary perspectives on attention patterns and their underlying computations.
- Attention-head view: The attention-head view displays token-to-token attention, with colors identifying heads and line weight representing attention scores.Users can select layers and heads, filter by tokens, and apply sentence-level filters for BERT.
- Attention-head view: Different heads learn distinct mechanisms, including previous-token, dispersed positional, within-sentence, and between-sentence attention patterns.GPT-2 and BERT examples illustrate how heads capture coarse positional and sentence-pair behaviors.
- Attention-head view: Attention heads also capture lexical and structural patterns, including list items, verbs, acronyms, named entities, punctuation, and syntactic relations.The attention-head view is also used to inspect potential gender bias in GPT-2 coreference behavior.
- Model View: The model view organizes heads in rows and columns to help users browse attention patterns across layers and locate relevant inter-sentence heads.Cross-hatch patterns can identify heads relevant to paraphrase detection, which users can then inspect in finer-grained views.
- Neuron View: The neuron view traces attention from a selected token through query-key interactions, while highlighting neuron positions associated with distance-decaying attention.In one GPT-2 head, query vectors were largely content-independent and a small number of positions appeared responsible for the decay pattern.
3 Conclusion
The paper introduced a multiscale Transformer attention visualization tool, demonstrated on GPT-2 and BERT through three use cases.
- The tool visualizes Transformer attention at multiple scales and was demonstrated on GPT-2 and BERT.
- The demonstrations covered three use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior.
- Future work includes unifying navigation across the three views, exposing value vectors and state activations, and enabling attention or neuron manipulation.