Summary of Ununlearning: Unlearning Is Not Sufficient For Content Regulation in Advanced Generative Ai, by Ilia Shumailov et al.
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
by Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jimenez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This paper revisits the concept of unlearning in Large Language Models (LLMs) as a means to control impermissible knowledge. Initially, exact unlearning was introduced as a privacy mechanism allowing users to retract their data from models on request. However, this approach is impractical due to its high costs. Inexact schemes were proposed to mitigate these costs. Recently, unlearning has been discussed as an approach for removing malicious information. The promise is that if the model doesn’t possess certain knowledge, it cannot be used for malicious purposes. We highlight an underlying inconsistency arising from in-context learning. Unlearning can control training phases but not prevent impermissible acts during inference. This paper introduces the concept of ununlearning, where forgotten knowledge gets reintroduced in-context, rendering models capable of behaving as if they know the forgotten information. Content filtering for impermissible knowledge will be required, even with exact unlearning schemes. We discuss feasibility for modern LLMs and examine broader implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to control what large language models learn. Right now, there’s a problem where these models can learn bad things on their own, like copyrighted material or inaccurate information. To fix this, some people have suggested “unlearning” the model so it forgets the bad information. But this isn’t perfect because it’s hard and expensive to make the model truly forget something. In fact, even if you do manage to make the model forget, it might still be able to behave as if it knows the forgotten information later on. The authors of this paper think that we need a new approach called “ununlearning” where the model is forced to relearn the bad information in a controlled way. This would help keep the model from doing bad things, even if it’s not perfect. |
Keywords
» Artificial intelligence » Inference
