Jay Alammar • 12/17/2020

Interfaces for Explaining Transformer Language Models

Read Original

This article presents interactive visualizations and explorables for explaining transformer-based language models, such as GPT-2. It covers input saliency methods to score token importance and neuron activation analysis to understand how model components generate outputs. It is the first in a series on model interpretability and is accompanied by an open-source library (Ecco) for creating similar interfaces in Jupyter notebooks.

0 comments
#Neural Networks #NLP #Language Models
Interfaces for Explaining Transformer Language Models

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
React, useEffect, and Stale Closures: How to Avoid Them with Dependencies and useRef
Jivbcoop • 4 votes
2
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
3
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
4
Quoting Thariq Shihipar
Simon Willison • 1 votes
5
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes
6
Using Browser Apis In React Practical Guide
Jivbcoop • 1 votes