Code for Large Language Model Unlearning via Embedding-Corrupted Prompts
-
Updated
Jun 28, 2024 - Python
Code for Large Language Model Unlearning via Embedding-Corrupted Prompts
A resource repository for machine unlearning in large language models
[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.
Continual Forgetting for Pre-trained Vision Models (CVPR 2024)
ConceptVectors Benchmark and Code for the paper "Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces"
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
Official implementation of "Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models"
The official implementation of the 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.
Implementation of paper 'Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference'
Awesome Machine Unlearning (A Survey of Machine Unlearning)
[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
"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning" by Chongyu Fan*, Jiancheng Liu*, Alfred Hero, Sijia Liu
Official Website of https://github.com/tamlhp/awesome-machine-unlearning
Official implementation of "Deeper Understanding of Black-box Predictions via Generalized Influence Functions".
[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
Unlearning Java, with Spring boot. AmigosCode https://youtu.be/9SGDpanrc8U
Repo on unlearning in FL. FYP22002@HKUCS.
Implementation for ICML 2022 paper: 'Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification'
Add a description, image, and links to the unlearning topic page so that developers can more easily learn about it.
To associate your repository with the unlearning topic, visit your repo's landing page and select "manage topics."