CoVUBench is the first benchmark framework for evaluating multimodal copyright unlearning in LVLMs via synthetic data, systematic variations, and a dual protocol for forgetting efficacy and utility preservation.
arXiv preprint arXiv:2006.14651 , year=
3 Pith papers cite this work, alongside 15 external citations. Polarity classification is still indexing.
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Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
D-Shap reformulates dynamic Shapley valuation as structured matrix maintenance exploiting utility and coalition locality to support millisecond task updates and orders-of-magnitude cheaper player updates.
citing papers explorer
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Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models
CoVUBench is the first benchmark framework for evaluating multimodal copyright unlearning in LVLMs via synthetic data, systematic variations, and a dual protocol for forgetting efficacy and utility preservation.
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Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
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Dynamic Shapley Computation
D-Shap reformulates dynamic Shapley valuation as structured matrix maintenance exploiting utility and coalition locality to support millisecond task updates and orders-of-magnitude cheaper player updates.