UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning
User specifications or legal frameworks often require information to be
removed from pretrained models, including large language models (LLMs). This
requires deleting or "forgetting" a set of data points from an already-trained
model, which typically degrades its performance on other data points. Thus, a
balance must be struck between removing information and keeping the model's
other abilities intact, with a failure to balance this trade-off leading to
poor deletion or an unusable model. To this end, we propose UPCORE
(Utility-Preserving Coreset Selection), a method-agnostic data selection
framework for mitigating collateral damage during unlearning. Finding that the
model damage is correlated with the variance of the model's representations on
the forget set, we selectively prune the forget set to remove outliers, thereby
minimizing model degradation after unlearning. We evaluate UPCORE across three
standard unlearning methods consistently achieving a superior balance between
the competing objectives of deletion efficacy and model preservation. To better
evaluate this trade-off, we introduce a new metric, measuring the
area-under-the-curve (AUC) across standard metrics. We find that UPCORE
improves both standard metrics and AUC, benefitting from positive transfer
between the coreset and pruned points while reducing negative transfer from the
forget set to points outside of it.
67bbe93f267aa2b537b318be
Vaidehi Patil
claimed_verified
2025-02-24T09:07:15.794Z
64f64da90efa33bfe0a3d9ba
/avatars/c45fb015433e46a2eeb9518910f75d35.svg
Vaidehi Patil
user
vaidehi99
67bbe93f267aa2b537b318bf
Elias Stengel-Eskin
admin_assigned
2025-02-24T16:16:49.662Z
61781c4caf41befe8ff060e8
/avatars/8871d7b046fc28cbc8638228da8e9737.svg
Elias Stengel-Eskin
user
esteng
67bbe93f267aa2b537b318c0
Mohit Bansal
admin_assigned
2025-02-24T16:16:57.627Z
665d9d3a057f7c508f98c625
https://cdn-avatars.huggingface.co/v1/production/uploads/665d9d3a057f7c508f98c625/u1R9P9sJoAl4zEIcetbPy.jpeg
Mohit Bansal
user
mohitbansal
UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning
User specifications or legal frameworks often require information to be
removed from pretrained models, including large language models (LLMs). This
requires deleting or "forgetting" a set of data points from an already-trained
model, which typically degrades its performance on other data points. Thus, a
balance must be struck between removing information and keeping the model's
other abilities intact, with a failure to balance this trade-off leading to
poor deletion or an unusable model. To this end, we propose UPCORE
(Utility-Preserving Coreset Selection), a method-agnostic data selection
framework for mitigating collateral damage during unlearning. Finding that the
model damage is correlated with the variance of the model's representations on
the forget set, we selectively prune the forget set to remove outliers, thereby
minimizing model degradation after unlearning. We evaluate UPCORE across three
standard unlearning methods consistently achieving a superior balance between
the competing objectives of deletion efficacy and model preservation. To better
evaluate this trade-off, we introduce a new metric, measuring the
area-under-the-curve (AUC) across standard metrics. We find that UPCORE
improves both standard metrics and AUC, benefitting from positive transfer
between the coreset and pruned points while reducing negative transfer from the
forget set to points outside of it.
UPCORE selectively prunes outliers from the forget set to balance data deletion and model preservation in unlearning processes.
unlearning pretrained models LLMs UPCORE coreset selection model degradation variance AUC