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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6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
Hybrid DP with LLM or NER preprocessing significantly improves the privacy-utility trade-off for Dutch clinical note de-identification compared to standalone DP.
Bangla Key2Text releases 2.6M keyword-text pairs and demonstrates that fine-tuned mT5 and BanglaT5 outperform zero-shot LLMs on keyword-conditioned Bangla text generation.
ESsEN is a parameter-efficient two-tower vision-language transformer that matches larger models on discriminative tasks after training end-to-end with limited data and resources.
Analysis of public submissions to the US AI Action Plan shows individuals emphasizing AI's societal impacts while the plan aligns more with private sector concerns on development, security, and policy.
A modular RAG pipeline with schema-constrained prompting, deterministic post-processing, and second-pass auditing reaches 80.36% F1 on observation extraction from nurse-patient transcripts using GPT-5.2.
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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Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation
Hybrid DP with LLM or NER preprocessing significantly improves the privacy-utility trade-off for Dutch clinical note de-identification compared to standalone DP.
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Bangla Key2Text: Text Generation from Keywords for a Low Resource Language
Bangla Key2Text releases 2.6M keyword-text pairs and demonstrates that fine-tuned mT5 and BanglaT5 outperform zero-shot LLMs on keyword-conditioned Bangla text generation.
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ESsEN: Training Compact Discriminative Vision-Language Transformers in a Low-Resource Setting
ESsEN is a parameter-efficient two-tower vision-language transformer that matches larger models on discriminative tasks after training end-to-end with limited data and resources.
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Whose Voice Counts? Mapping Stakeholder Perspectives on AI Through Public Submissions to the U.S. Government
Analysis of public submissions to the US AI Action Plan shows individuals emphasizing AI's societal impacts while the plan aligns more with private sector concerns on development, security, and policy.
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Retrieval-Augmented Large Language Models for Schema-Constrained Clinical Information Extraction
A modular RAG pipeline with schema-constrained prompting, deterministic post-processing, and second-pass auditing reaches 80.36% F1 on observation extraction from nurse-patient transcripts using GPT-5.2.