title: TOFU: A Task of Fictitious Unlearning for LLMs
summary: Large language models trained on massive corpora of data from the web can
  memorize and reproduce sensitive or private data raising both legal and ethical
  concerns. Unlearning, or tuning models to forget information present in their
  training data, provides us with a way to protect private data after training.
  Although several methods exist for such unlearning, it is unclear to what
  extent they result in models equivalent to those where the data to be forgotten
  was never learned in the first place. To address this challenge, we present
  TOFU, a Task of Fictitious Unlearning, as a benchmark aimed at helping deepen
  our understanding of unlearning. We offer a dataset of 200 diverse synthetic
  author profiles, each consisting of 20 question-answer pairs, and a subset of
  these profiles called the forget set that serves as the target for unlearning.
  We compile a suite of metrics that work together to provide a holistic picture
  of unlearning efficacy. Finally, we provide a set of baseline results from
  existing unlearning algorithms. Importantly, none of the baselines we consider
  show effective unlearning motivating continued efforts to develop approaches
  for unlearning that effectively tune models so that they truly behave as if
  they were never trained on the forget data at all.
paper_authors._id: 65a0ac08b90a1eb50fe0afff
paper_authors.name: Pratyush Maini
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-12T10:02:37.531Z
paper_authors._id: 632c826d1d303f5f9acf5917
paper_authors.avatarUrl: /avatars/defca406f2c4f027a7b1be9a5ebbbd01.svg
paper_authors.fullname: Pratyush Maini
paper_authors.type: user
paper_authors.user: pratyushmaini
paper_authors._id: 65a0ac08b90a1eb50fe0b000
paper_authors.name: Zhili Feng
paper_authors._id: 65a0ac08b90a1eb50fe0b001
paper_authors.name: Avi Schwarzschild
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-01-12T12:37:40.724Z
paper_authors._id: 6570917c0ea91e592aff0b8c
paper_authors.avatarUrl: /avatars/529e9713e6ac835e11599ea7070a9603.svg
paper_authors.fullname: Avi Schwarzschild
paper_authors.type: user
paper_authors.user: schwarzschild
paper_authors._id: 65a0ac08b90a1eb50fe0b002
paper_authors.name: Zachary C. Lipton
paper_authors._id: 65a0ac08b90a1eb50fe0b003
paper_authors.name: J. Zico Kolter
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-12T10:03:33.415Z
paper_authors._id: 6561eb612cd8dac568605da3
paper_authors.avatarUrl: /avatars/6d145d7e225b16bde64680ba8c05f856.svg
paper_authors.fullname: Zico Kolter
paper_authors.type: user
paper_authors.user: zkolter
paper_title: TOFU: A Task of Fictitious Unlearning for LLMs
paper_summary: Large language models trained on massive corpora of data from the web can
  memorize and reproduce sensitive or private data raising both legal and ethical
  concerns. Unlearning, or tuning models to forget information present in their
  training data, provides us with a way to protect private data after training.
  Although several methods exist for such unlearning, it is unclear to what
  extent they result in models equivalent to those where the data to be forgotten
  was never learned in the first place. To address this challenge, we present
  TOFU, a Task of Fictitious Unlearning, as a benchmark aimed at helping deepen
  our understanding of unlearning. We offer a dataset of 200 diverse synthetic
  author profiles, each consisting of 20 question-answer pairs, and a subset of
  these profiles called the forget set that serves as the target for unlearning.
  We compile a suite of metrics that work together to provide a holistic picture
  of unlearning efficacy. Finally, we provide a set of baseline results from
  existing unlearning algorithms. Importantly, none of the baselines we consider
  show effective unlearning motivating continued efforts to develop approaches
  for unlearning that effectively tune models so that they truly behave as if
  they were never trained on the forget data at all.
paper_ai_keywords: large language models sensitive data private data legal concerns ethical concerns unlearning tuning models forget information TOFU Task of Fictitious Unlearning benchmark synthetic author profiles question-answer pairs forget set metrics unlearning efficacy baseline results existing unlearning algorithms