[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.

Awesome Machine Unlearning (A Survey of Machine Unlearning)

A resource repository for machine unlearning in large language models

[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu

[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models. NeurIPS 2024

The official implementation of ECCV'24 paper "To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now". This work introduces one fast and effective attack method to evaluate the harmful-content generation ability of safety-driven unlearned diffusion models.

[ACL 2024] Code and data for "Machine Unlearning of Pre-trained Large Language Models"

Continual Forgetting for Pre-trained Vision Models (CVPR 2024)

ConceptVectors Benchmark and Code for the paper "Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces"

Code for implementation of Unlearning Scanner Bias for MRI Harmonisation

Official implementation of NeurIPS'24 paper "Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models". This work adversarially unlearns the text encoder to enhance the robustness of unlearned DMs against adversarial prompt attacks and achieves a better balance between unlearning performance and image generation

[EMNLP 2024 Findings] To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models

Implementation for MICCAI DART paper: 'Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification'

[ECCV24] "Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning" by Chongyu Fan*, Jiancheng Liu*, Alfred Hero, Sijia Liu

Pytorch implementation of backdoor unlearning.

Implementation of paper 'Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference' [NeurIPS'24]

Official Code of Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers (AAAI 2024)

[NeurIPS 2024] Large Language Model Unlearning via Embedding-Corrupted Prompts