In this work, we focus on utilizing LLMs (Large Language Models) for optimizing natural language tasks. Our approach, termed OPRO, allows for efficient adaptation using limited training data.
We illustrate how OPRO operates through meta-prompt design, providing a systematic way to improve task accuracy across various natural language processing challenges while ensuring generalization beyond training scenarios.
#natural-language-processing #prompt-optimization #machine-learning #google-deepmind #optimization-techniques
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