Stanford NLP Python Library for Understanding and Improving PyTorch Models via Interventions

This repository collects all relevant resources about interpretability in LLMs

Decomposing and Editing Predictions by Modeling Model Computation

Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms

Interpreting how transformers simulate agents performing RL tasks

Steering vectors for transformer language models in Pytorch / Huggingface

For OpenMOSS Mechanistic Interpretability Team's Sparse Autoencoder (SAE) research.

🧠 Starter templates for doing interpretability research

Repo accompanying our paper "Do Llamas Work in English? On the Latent Language of Multilingual Transformers".

Sparse and discrete interpretability tool for neural networks

Sparse probing paper full code.

Generating and validating natural-language explanations.

CausalGym: Benchmarking causal interpretability methods on linguistic tasks

Universal Neurons in GPT2 Language Models

Arrakis is a library to conduct, track and visualize mechanistic interpretability experiments.

This repository contains the code used for the experiments in the paper "Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking".

Code for "Preference Tuning For Toxicity Mitigation Generalizes Across Languages." Paper accepted at Findings of EMNLP 2024

PyTorch and NNsight implementation of AtP* (Kramar et al 2024, DeepMind)

CoSy: Evaluating Textual Explanations

Multi-Layer Sparse Autoencoders