✨AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models

📝 Summary:
This study empirically analyzes visual token pruning in LVLMs. It finds attention-based pruning is better for simple images, while diversity-based methods suit complex ones. These insights lead to improved adaptive pruning strategies that reduce hallucination.

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01236
• PDF: https://arxiv.org/pdf/2603.01236
• Project Page: https://paper.pnu-cvsp.com/AgilePruner/
• Github: https://github.com/cvsp-lab/AgilePruner

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#LVLMs #VisualTokenPruning #AdaptiveAI #HallucinationReduction #DeepLearning

✨Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs

📝 Summary:
XKD-Dial is a progressive training pipeline for explainable, bilingual English-Hindi knowledge-grounded dialogue. It achieves zero hallucination rates by using citation grounding and improves explainability through post-hoc analyses.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18911
• PDF: https://arxiv.org/pdf/2603.18911

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#LLMs #ExplainableAI #NaturalLanguageProcessing #AIResearch #HallucinationReduction