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cs.CL2025
Reclaiming Low-Quality Instruction Data with Rule-Guided LLM Reconstruction
Zile Yang, Ling Li, Na Di +5
Data selection for supervised fine-tuning (SFT) often discards low-scoring instruction examples that may retain useful supervision. We propose a rule-guided LLM reconstruction fram…
cs.CL2025
LM-mixup: Text Data Augmentation via Language Model based Mixup
Zhijie Deng, Zhouan Shen, Ling Li +5
Instruction tuning is crucial for aligning Large Language Models (LLMs), yet the quality of instruction-following data varies significantly. While high-quality data is paramount, i…