SFT-GO retrains LLMs by focusing on the worst-performing group of important or unimportant tokens, yielding modest average benchmark improvements over standard supervised fine-tuning.
From quantity to quality: Boosting LLM performance with self- guided data selection for instruction tuning
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SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
SFT-GO retrains LLMs by focusing on the worst-performing group of important or unimportant tokens, yielding modest average benchmark improvements over standard supervised fine-tuning.