UNIFIED IN-CONTEXT PROMPT OPTIMIZATION FOR LARGE LANGUAGE MODELS

原文（中国語）

Certain aspects of the disclosure provide unified in-context prompt optimization for large language models that achieves joint optimization of prompt instruction and examples. A multi-phase approach is provided that includes multiple mutation operations. Further, the approach alternates between optimization strategies for exploration for global search and exploitation for local search. Global initialization creates a diverse set of candidate prompts based on the availability of data and utilizing Lamarckian or semantic mutation. Local feedback mutation, global evolution mutation, and local semantic mutation can subsequently be employed iteratively to generate a revised set of candidate prompts. A prompt from the revised set of candidate prompts can be selected based on an evaluation of the candidate prompts. Subsequently, the selected prompt can be output for a machine-learning task.