Rank-One Model Editing for Locating and Editing Factual Knowledge in GPT

Overview

Rank-One Model Editing (ROME)

This repository provides an implementation of Rank-One Model Editing (ROME) on auto-regressive transformers (GPU-only). We currently support OpenAI's GPT-2 XL (1.5B) and EleutherAI's GPT-J (6B). The release of a 20B GPT-like model from EleutherAI is expected soon; we hope to support it ASAP.

Feel free to open an issue if you find any problems; we are actively developing this repository and will monitor tickets closely.

Colab ROME Demo

causal tracing GIF

Table of Contents

  1. Installation
  2. Causal Tracing
  3. Rank-One Model Editing (ROME)
  4. CounterFact Dataset
  5. Evaluation
  6. How to Cite

Installation

We recommend conda for managing Python, CUDA, and PyTorch-related dependencies, and pip for everything else. To get started, simply install conda and run:

./scripts/setup_conda.sh

Causal Tracing

notebooks/causal_trace.ipynb demonstrates Causal Tracing, which can be modified to apply tracing to the processing of any statement.

causal tracing GIF

Rank-One Model Editing (ROME)

notebooks/rome.ipynb demonstrates ROME. The API is simple; one simply has to specify a requested rewrite of the following form:

request = {
    "prompt": "{} plays the sport of",
    "subject": "LeBron James",
    "target_new": {
        "str": "football"
    }
}

Several similar examples are included in the notebook.

CounterFact

Description coming soon!

Evaluation

Paper Baselines

We compare ROME against several state-of-the-art model editors. All are implemented in baselines/ in their respective folders. Implementations are not our own; they are adapted slightly to plug into our evaluation system.

Running the Full Evaluation Suite

Description coming soon!

How to Cite

@article{meng2022locating,
  title={Locating and Editing Factual Knowledge in GPT},
  author={Kevin Meng and David Bau and Alex Andonian and Yonatan Belinkov},
  journal={arXiv preprint arXiv:2202.05262},
  year={2022}
}
Owner
Kevin Meng
MIT ugrad interested in interpretability and its applications to NLP, bioinformatics, and robotics.
Kevin Meng
Predict the spans of toxic posts that were responsible for the toxic label of the posts

toxic-spans-detection An attempt at the SemEval 2021 Task 5: Toxic Spans Detection. The Toxic Spans Detection task of SemEval2021 required participant

Ilias Antonopoulos 3 Jul 24, 2022
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Microsoft 105 Jan 08, 2022
构建一个多源(公众号、RSS)、干净、个性化的阅读环境

2C 构建一个多源(公众号、RSS)、干净、个性化的阅读环境 作为一名微信公众号的重度用户,公众号一直被我设为汲取知识的地方。随着使用程度的增加,相信大家或多或少会有一个比较头疼的问题——广告问题。 假设你关注的公众号有十来个,若一个公众号两周接一次广告,理论上你会面临二十多次广告,实际上会更多,运

howie.hu 678 Dec 28, 2022
HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools

HuggingSound HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools. I have no intention of building a very complex tool here.

Jonatas Grosman 247 Dec 26, 2022
NLTK Source

Natural Language Toolkit (NLTK) NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting

Natural Language Toolkit 11.4k Jan 04, 2023
ACL'22: Structured Pruning Learns Compact and Accurate Models

☕ CoFiPruning: Structured Pruning Learns Compact and Accurate Models This repository contains the code and pruned models for our ACL'22 paper Structur

Princeton Natural Language Processing 130 Jan 04, 2023
texlive expressions for documents

tex2nix Generate Texlive environment containing all dependencies for your document rather than downloading gigabytes of texlive packages. Installation

Jörg Thalheim 70 Dec 26, 2022
Rootski - Full codebase for rootski.io (without the data)

📣 Welcome to the Rootski codebase! This is the codebase for the application run

Eric 20 Nov 18, 2022
Anomaly Detection 이상치 탐지 전처리 모듈

Anomaly Detection 시계열 데이터에 대한 이상치 탐지 1. Kernel Density Estimation을 활용한 이상치 탐지 train_data_path와 test_data_path에 존재하는 시점 정보를 포함하고 있는 csv 형태의 train data와

CLUST-consortium 43 Nov 28, 2022
Pangu-Alpha for Transformers

Pangu-Alpha for Transformers Usage Download MindSpore FP32 weights for GPU from here to data/Pangu-alpha_2.6B.ckpt Activate MindSpore environment and

One 5 Oct 01, 2022
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0 🤗 Transformers provides thousands of pretrained models to perform tasks o

Hugging Face 77.3k Jan 03, 2023
A simple tool to update bib entries with their official information (e.g., DBLP or the ACL anthology).

Rebiber: A tool for normalizing bibtex with official info. We often cite papers using their arXiv versions without noting that they are already PUBLIS

(Bill) Yuchen Lin 2k Jan 01, 2023
Tool to check whether a GCP bucket is public or not.

Tool to check publicly accessible GCP bucket. Blog https://justm0rph3u5.medium.com/gcp-inspector-auditing-publicly-exposed-gcp-bucket-ac6cad55618c Wha

DIVYANSHU SHUKLA 7 Nov 24, 2022
An open source framework for seq2seq models in PyTorch.

pytorch-seq2seq Documentation This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and

International Business Machines 1.4k Jan 02, 2023
Various Algorithms for Short Text Mining

Short Text Mining in Python Introduction This package shorttext is a Python package that facilitates supervised and unsupervised learning for short te

Kwan-Yuet 466 Dec 06, 2022
A simple Flask site that allows users to create, update, and delete posts in a database, as well as perform basic NLP tasks on the posts.

A simple Flask site that allows users to create, update, and delete posts in a database, as well as perform basic NLP tasks on the posts.

Ian 1 Jan 15, 2022
Residual2Vec: Debiasing graph embedding using random graphs

Residual2Vec: Debiasing graph embedding using random graphs This repository contains the code for S. Kojaku, J. Yoon, I. Constantino, and Y.-Y. Ahn, R

SADAMORI KOJAKU 5 Oct 12, 2022
Code for the ACL 2021 paper "Structural Guidance for Transformer Language Models"

Structural Guidance for Transformer Language Models This repository accompanies the paper, Structural Guidance for Transformer Language Models, publis

International Business Machines 10 Dec 14, 2022
Weakly-supervised Text Classification Based on Keyword Graph

Weakly-supervised Text Classification Based on Keyword Graph How to run? Download data Our dataset follows previous works. For long texts, we follow C

Hello_World 20 Dec 29, 2022
Baseline code for Korean open domain question answering(ODQA)

Open-Domain Question Answering(ODQA)는 다양한 주제에 대한 문서 집합으로부터 자연어 질의에 대한 답변을 찾아오는 task입니다. 이때 사용자 질의에 답변하기 위해 주어지는 지문이 따로 존재하지 않습니다. 따라서 사전에 구축되어있는 Knowl

VUMBLEB 69 Nov 04, 2022