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SoK: Machine Unlearning for Large Language Models
- Large language model (LLM) unlearning is crucial in machine learning to remove the influence of specific training data without retraining the model entirely.
- Techniques like Gradient Ascent, model editing, and re-steering hidden representations have been proposed for LLM unlearning.
- An intention-oriented taxonomy is proposed in the paper to classify unlearning methods based on whether they aim to truly remove internal knowledge or just suppress its effects.
- The paper revisits findings suggesting that many removal methods may functionally behave like suppression and explores the necessity and achievability of true removal.
- Existing evaluation strategies for unlearning are surveyed, current metrics and benchmarks are critiqued, and suggestions for more reliable evaluations are provided.
- Practical challenges like scalability and support for sequential unlearning in the broader deployment of unlearning methods are highlighted.
- This work aims to provide a comprehensive framework for understanding and advancing unlearning in generative AI, supporting future research and guiding policy decisions on data removal and privacy.
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