iPOE derives and optimizes guidelines from explanations to create interpretable prompts, yielding up to 31% and 35% gains over standard and random-guideline prompts on four datasets.
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A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.
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iPOE: Interpretable Prompt Optimization via Explanations
iPOE derives and optimizes guidelines from explanations to create interpretable prompts, yielding up to 31% and 35% gains over standard and random-guideline prompts on four datasets.
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A Survey on Knowledge Distillation of Large Language Models
A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.