title: UnUnlearning: Unlearning is not sufficient for content regulation in
    advanced generative AI
summary: Exact unlearning was first introduced as a privacy mechanism that allowed a
  user to retract their data from machine learning models on request. Shortly
  after, inexact schemes were proposed to mitigate the impractical costs
  associated with exact unlearning. More recently unlearning is often discussed
  as an approach for removal of impermissible knowledge i.e. knowledge that the
  model should not possess such as unlicensed copyrighted, inaccurate, or
  malicious information. The promise is that if the model does not have a certain
  malicious capability, then it cannot be used for the associated malicious
  purpose. In this paper we revisit the paradigm in which unlearning is used for
  in Large Language Models (LLMs) and highlight an underlying inconsistency
  arising from in-context learning. Unlearning can be an effective control
  mechanism for the training phase, yet it does not prevent the model from
  performing an impermissible act during inference. We introduce a concept of
  ununlearning, where unlearned knowledge gets reintroduced in-context,
  effectively rendering the model capable of behaving as if it knows the
  forgotten knowledge. As a result, we argue that content filtering for
  impermissible knowledge will be required and even exact unlearning schemes are
  not enough for effective content regulation. We discuss feasibility of
  ununlearning for modern LLMs and examine broader implications.
paper_authors._id: 6683b18f5f3099b62dd0555f
paper_authors.name: Ilia Shumailov
paper_authors._id: 6683b18f5f3099b62dd05560
paper_authors.name: Jamie Hayes
paper_authors._id: 6683b18f5f3099b62dd05561
paper_authors.name: Eleni Triantafillou
paper_authors._id: 6683b18f5f3099b62dd05562
paper_authors.name: Guillermo Ortiz-Jimenez
paper_authors._id: 6683b18f5f3099b62dd05563
paper_authors.name: Nicolas Papernot
paper_authors._id: 6683b18f5f3099b62dd05564
paper_authors.name: Matthew Jagielski
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T15:09:39.176Z
paper_authors._id: 654a4a3b6a46bad8c0f2ab80
paper_authors.avatarUrl: /avatars/08caf74525ea3e46fe58c20eb1f3d64d.svg
paper_authors.fullname: Matthew Jagielski
paper_authors.type: user
paper_authors.user: jagielski
paper_authors._id: 6683b18f5f3099b62dd05565
paper_authors.name: Itay Yona
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T15:09:33.536Z
paper_authors._id: 62697edaa6a7bba9e46ae4ad
paper_authors.avatarUrl: /avatars/3009bf769a09d946447a0e8c833a04f3.svg
paper_authors.fullname: Itay Yona
paper_authors.type: user
paper_authors.user: tux
paper_authors._id: 6683b18f5f3099b62dd05566
paper_authors.name: Heidi Howard
paper_authors._id: 6683b18f5f3099b62dd05567
paper_authors.name: Eugene Bagdasaryan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T15:09:19.593Z
paper_authors._id: 61ae7dc0f3f39c685adeb738
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1638978358427-61ae7dc0f3f39c685adeb738.jpeg
paper_authors.fullname: Eugene Bagdasaryan
paper_authors.type: user
paper_authors.user: ebagdasa
paper_title: UnUnlearning: Unlearning is not sufficient for content regulation in
    advanced generative AI
paper_summary: Exact unlearning was first introduced as a privacy mechanism that allowed a
  user to retract their data from machine learning models on request. Shortly
  after, inexact schemes were proposed to mitigate the impractical costs
  associated with exact unlearning. More recently unlearning is often discussed
  as an approach for removal of impermissible knowledge i.e. knowledge that the
  model should not possess such as unlicensed copyrighted, inaccurate, or
  malicious information. The promise is that if the model does not have a certain
  malicious capability, then it cannot be used for the associated malicious
  purpose. In this paper we revisit the paradigm in which unlearning is used for
  in Large Language Models (LLMs) and highlight an underlying inconsistency
  arising from in-context learning. Unlearning can be an effective control
  mechanism for the training phase, yet it does not prevent the model from
  performing an impermissible act during inference. We introduce a concept of
  ununlearning, where unlearned knowledge gets reintroduced in-context,
  effectively rendering the model capable of behaving as if it knows the
  forgotten knowledge. As a result, we argue that content filtering for
  impermissible knowledge will be required and even exact unlearning schemes are
  not enough for effective content regulation. We discuss feasibility of
  ununlearning for modern LLMs and examine broader implications.
paper_ai_summary: Unlearning in Large Language Models is found to be ineffective due to in-context learning, implying that additional content filtering mechanisms are necessary.
paper_ai_keywords: unlearning Large Language Models in-context learning ununlearning content filtering