herbwood27/Remem
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks Abstract While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal information. Current unlearning benchmarks attempt to mitigate this using fictitious identities but overlook a critical stage 1 failure: models fail to effectively memorize target information initially… See the full description on the dataset page: https://huggingface.co/datasets/herbwood27/Remem.
| Author / organization | herbwood27 |
|---|---|
| Downloads | 235 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 7 April 2026 |
| Last updated at source | 6 May 2026 |
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- 16 Oct 2026 Description: Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks Abstract While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memori → Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks Abstract While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memori
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