MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning 事件

MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning arXiv:2605.30857v1 Announce Type: new Abstract: Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuning data increases, selecting the optimal core set becomes particularly important. However, ensuring the diversity of the core set remains a significant challenge. Existing methods predominantly distinguish different training data b