Mengmeng Guo

JI2S: Joint Influence‐Aware Instruction Data Selection for Efficient Fine‐Tuning

Instruction tuning (IT) improves large language models (LLMs) by aligning their outputs with human instructions, but its success depends critically on training data quality, and datasets such as Alpaca often contain noisy or suboptimal examples that undermine fine‐tuning. Prior selection strategies