RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair
Large language models (LLMs) inherently absorb harmful knowledge, misinformation, and personal data during pretraining on large-scale web corpora, with no native mechanism for selective removal. While machine unlearning offers a principled solution, existing approaches are provider-centric, requiring retraining pipelines, curated retain datasets, and direct intervention by model service providers (MSPs), thereby excluding end users from controlling their own data. We introduce Interactive Machine Unlearning (IMU), a new paradigm in which users can instruct LLMs to forget targeted knowledge through natural language at inference time. To realize IMU, we propose RePAIR, a prompt-aware model ...
| Authors | Jagadeesh Rachapudi, Pranav Singh, Ritali Vatsi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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