SoK: Machine Unlearning for Large Language Models
Neuradyne Team
June 10, 2025
7 min read
Large language models may unintentionally memorize sensitive training data. Machine unlearning aims to remove the influence of specific data points without retraining models from scratch.
Key Contribution
The paper introduces an intent-based taxonomy distinguishing true data removal from behavioral suppression.
Method Review
The survey analyzes approaches such as gradient ascent, model editing, and representation steering, evaluating them across benchmarks derived from public datasets.
Demonstrates large efficiency gains over full retraining
Why This Matters
Machine unlearning is increasingly important for compliance with privacy regulations such as GDPR and for building trustworthy AI systems.
SoK: Machine Unlearning for Large Language Models
LLMUnlearningPrivacy