Interference-Aware Multi-Task Unlearning
The paper introduces a framework for multi-task unlearning in machine learning, addressing the challenges of removing specific training data without affecting other tasks. It presents two unlearning settings: full-task and partial-task unlearning, and highlights the interference caused by shared parameters. The proposed method demonstrates significant improvements in unlearning effectiveness while maintaining model generalization across multiple tasks.
- ▪Machine unlearning aims to remove designated training data from a model while preserving performance on remaining data.
- ▪The proposed framework combines task-aware gradient projection with instance-level gradient orthogonalization to reduce interference.
- ▪Experiments show a 30.3% reduction in unlearning interference score for full-task unlearning and 52.9% for partial-task unlearning.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19042 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | XDA9i6VYhTNL |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.19042 (cs) [Submitted on 18 May 2026] Title:Interference-Aware Multi-Task Unlearning Authors:Ying-Hua Huang, Rui Fang, Hsi-Wen Chen, Ming-Syan Chen View a PDF of the paper titled Interference-Aware Multi-Task Unlearning, by Ying-Hua Huang and 3 other authors View PDF HTML (experimental) Abstract:Machine unlearning aims to remove the contribution of designated training data from a trained model while preserving performance on the remaining data. Existing work mainly focuses on single-task settings, whereas modern models often operate in multi-task setups with shared backbones, where removing supervision for one task or instance can unintentionally affect others.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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