Fine-tuning only on questions a model answers incorrectly reaches nearly the same performance as full-data fine-tuning while substantially reducing training time.
In: 2016 IEEE Conference on Computational In- telligence and Games (CIG)
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SLearnLLM: A Self-Learning Framework for Efficient Domain-Specific Adaptation of Large Language Models
Fine-tuning only on questions a model answers incorrectly reaches nearly the same performance as full-data fine-tuning while substantially reducing training time.