Prompt Engineering And Instruction Optimization With Few Shot Learning

via “few-shot learning and in-context adaptation”

text-generation model by undefined. 94,68,562 downloads.

Unique: Few-shot learning emerges from transformer attention mechanisms learning patterns from in-context examples without explicit meta-learning modules; enables rapid task adaptation by processing examples as part of input context, avoiding fine-tuning overhead

vs others: Faster task adaptation than fine-tuning-based approaches; comparable to GPT-3.5 on few-shot performance but with local control; outperforms Mistral-7B on instruction-following few-shot tasks due to explicit instruction tuning