AI RESEARCH
MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning
arXiv CS.CL
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ArXi:2605.30857v1 Announce Type: new 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