Tutorial for α,β-CROWN Robustness Verification

α,β-CROWN won the competition for robustness verification (VNN-COMP'22). Also see this other tutorial.

The basic idea behind α,β-CROWN is to use efficient bound propagation for verification tasks based on Automatic Linear Relaxation based Perturbation Analysis for Neural Networks (LiRPA). The code of LiRPA can be found on github.

Adversarial attacks against large language models

The TextAttack library 🐙 is a set of tools for creating adversarial examples for large language models. See the documentation.

Maybe you wish to use it to compete in the HackAPrompt competition as well which has extended its deadline until the 4th of June!

Watch a WANDB talk about the library.

Auto-LiRPA tutorial

LiRPA is an important tool in robustness verification and certified adversarial defense and this tutorial takes you through the basics.

See the documentation and their introductory video to the tool.

Running TransformerLens to easily analyze activations in language models

This demo notebook goes into depth on how to use the TransformerLens library. TransformerLens is very useful for mechanistic investigations into Transformers and can be very useful for understanding activations. Core features:

1. Loading and running models
2. Saving activations from a specific example run
3. Using the unique Hooks functionality to intervene on and access activations

Read more on the Github page and see the Python package on PyPi.

System 3 continuation

The System 3 paper by our research lead Fazl Barez uses an approach to input symbolic logic for safety in natural environments into neural networks. This Colab notebook is an experimental setting and we recommend you read the paper first.