ERASURE: Redefining Privacy Through Selective Machine Unlearning
The LLM Unlearning repository is an open-source project dedicated to the concept of unlearning in Large Language Models (LLMs). It aims to address concerns about data privacy and ethical AI by exploring and implementing unlearning techniques that allow models to forget unwanted or sensitive data. This ensures that AI models comply with privacy.
"Machine Unlearning Fails to Remove Data Poisoning Attacks". Pawelczyk, M., Di, J., Lu, Y., Sekhari, A., Gautam, K., Seth, N.; ICLR 2025.
The project is for LLM unlearning, trustworthy AI.
Multi-GPU framework for reproducible image-unlearning evaluation, with independent training, unlearning and evaluation seeds and extensible datasets, models, methods and metrics. Published at WIPE-OUT 2 (ECML-PKDD 2026).
Lethe is a comprehensive Python library for machine unlearning - the process of selectively removing the influence of specific training data from machine learning models.
Reproducibility of CRISP: Persistent Concept Unlearning via Sparse Autoencoders[BluedotAI Technical AI Safety Project]
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