Meta-prompting is a technique that enhances the functionality of language models by transforming them into multi-faceted conductors. It guides the models to break down complex tasks into smaller subtasks, which are handled by expert instances of the same model. This approach improves performance across various tasks and simplifies user interaction. The integration of external tools, such as a Python interpreter, further expands its applicability. Experimental results show that meta-prompting outperforms conventional scaffolding methods.

https://arxiv.org/abs//2401.12954

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