Large Language Models Are Human-Level Prompt Engineers - Summary
Blog post from Portkey
Automatic Prompt Engineer (APE) is an algorithm designed to enhance the performance of large language models (LLMs) by generating and selecting optimal natural language instructions. APE treats instructions like programs and optimizes them by exploring a pool of candidates suggested by an LLM to maximize a defined score function, with the selected instruction being evaluated for zero-shot performance by another LLM. This method not only surpasses prior LLM baselines but also achieves performance comparable to human-level prompt engineering across various tasks. While APE shows promise in improving few-shot learning and finding effective zero-shot chain-of-thought prompts, it requires additional computational resources and is limited by the quality of the scoring function and task-specific optimization needs. The approach leverages technologies like PyTorch and Hugging Face Transformers, focusing on concepts such as natural language program synthesis and inference model optimization.
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