# Visualizing Attention in Transformer-Based Language models

**Authors:** Jesse Vig  
**arXiv:** [1904.02679](https://arxiv.org/abs/1904.02679)  
**Date:** 2019-04-04  
**Summary page:** https://www.summarizepaper.com/en/paper/1904.02679v1/

## Key points

- Open-source tool for visualizing multi-head self-attention in Transformer-based language models
- Visualizations at three levels of granularity: attention-head level, model level, and neuron level
- Helps interpret the inner workings of the model and gain insights into its decision-making process
- Effectiveness demonstrated by applying it to OpenAI GPT-2 pretrained language model
- Each visualization view provides valuable information about different aspects of the model's attention mechanism
- Identifying biases in language generation
- Detecting over-reliance on certain tokens or positions
- Guiding fine-tuning strategies for specific downstream tasks
- Comprehensive visualization tool contributes to understanding of Transformer-based language models
- Visualizations can enhance model performance and address potential limitations.

## Summary

The paper presents an open-source tool for visualizing multi-head self-attention in Transformer-based language models. The tool builds upon previous work by providing visualizations at three levels of granularity: the attention-head level, the model level, and the neuron level. These visualizations help researchers and practitioners interpret the inner workings of the model and gain insights into its decision-making process. The authors demonstrate the effectiveness of the tool by applying it to the OpenAI GPT-2 pretrained language model. They showcase how each visualization view can provide valuable information about different aspects of the model's attention mechanism. Furthermore, the paper highlights three specific use cases where the visualization tool can offer insights on how to adapt or improve language models. These use cases demonstrate how analyzing attention patterns can help identify biases in language generation, detect over-reliance on certain tokens or positions, and guide fine-tuning strategies for specific downstream tasks. Overall, this work contributes to our understanding of Transformer-based language models by providing a comprehensive visualization tool that enables researchers and practitioners to explore and interpret their inner workings. The presented use cases illustrate how these visualizations can be leveraged to enhance model performance and address potential limitations.