What to Forget in Unlearning? Forget Set Curation for Language Models
Machine unlearning aims to remove targeted data or behaviors from a trained model without retraining from scratch. Yet most evaluations assume that the examples to forget are already known. In realistic language-model deployments, a requester may ask a model to stop reproducing a song or book without knowing which spans, documents, quotations, or near-duplicates in a trillion-token corpus support that behavior. We study this missing upstream problem, forget set curation: mapping a suppression request to the data passed to an unlearning algorithm. We introduce CleanSlate, a benchmark for verbatim output suppression over songs and books, with model-specific extraction profiles, content-grou...
| Authors | Animesh Jha, Arpandeep Khatua, Youssef Allouah et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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