TOFU: A Task of Fictitious Unlearning for LLMs
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.
65a0ac08b90a1eb50fe0afff
Pratyush Maini
admin_assigned
2024-01-12T10:02:37.531Z
632c826d1d303f5f9acf5917
/avatars/defca406f2c4f027a7b1be9a5ebbbd01.svg
Pratyush Maini
user
pratyushmaini
65a0ac08b90a1eb50fe0b000
Zhili Feng
65a0ac08b90a1eb50fe0b001
Avi Schwarzschild
extracted_confirmed
2024-01-12T12:37:40.724Z
6570917c0ea91e592aff0b8c
/avatars/529e9713e6ac835e11599ea7070a9603.svg
Avi Schwarzschild
user
schwarzschild
65a0ac08b90a1eb50fe0b002
Zachary C. Lipton
65a0ac08b90a1eb50fe0b003
J. Zico Kolter
admin_assigned
2024-01-12T10:03:33.415Z
6561eb612cd8dac568605da3
/avatars/6d145d7e225b16bde64680ba8c05f856.svg
Zico Kolter
user
zkolter
TOFU: A Task of Fictitious Unlearning for LLMs
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.
TOFU, a benchmark for evaluating unlearning in large language models, uses synthetic data to measure how effectively unlearning methods can remove specific information from the model.
large language models unlearning TOFU Task of Fictitious Unlearning synthetic author profiles forget set metrics baseline results