Summary of Machine Unlearning For Traditional Models and Large Language Models: a Short Survey, by Yi Xu
Machine Unlearning for Traditional Models and Large Language Models: A Short Survey
by Yi Xu
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators can benefit from this comprehensive survey on machine unlearning, which tackles the “right to be forgotten” challenge posed by personal data privacy regulations. The paper provides an in-depth exploration of machine unlearning’s definition, classification, and evaluation criteria, including challenges and solutions in various environments. Specifically, it categorizes and investigates unlearning on traditional models and Large Language Models (LLMs), proposing methods for evaluating effectiveness and efficiency, as well as standards for performance measurement. The survey highlights the limitations of current techniques and emphasizes the importance of a comprehensive unlearning evaluation to avoid arbitrary forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has a new challenge: “right to be forgotten”. This means machines must forget some information when people ask them to. Researchers have been working on this problem, but there’s no big picture view yet. This paper helps by explaining what machine unlearning is, how it works, and why it matters. It also talks about the different types of models that can be “unlearned” and how to measure if it’s working well. The survey says we need better ways to test this technology so we don’t accidentally forget important things. |
Keywords
» Artificial intelligence » Classification » Machine learning
