PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection 事件

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection arXiv:2502.12119v4 Announce Type: replace Abstract: Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications. However, the rapid growth of these datasets introduces significant redundancy, leading to increased computational costs. Existing methods for selecting instruction data aim to prune this redundancy, but predomina