From Monolithic to Modular: Segment-level Automatic Prompt Optimization

From Monolithic to Modular: Segment-level Automatic Prompt Optimization

Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others. We present SAPO, a segment-level APO method that decomposes prompts into role, context, tasks, and output format, then applies targeted improvements based on top-5 and bottom-5 examples. The optimization loop uses one LLM with static meta-prompts and structured outputs for segmentation, weakness analysis, and candidate generation. We describe a train/validation protocol and a two-stage generation process: (1) segment-level diagnosis and recommendation extraction, (2) candidate synthesis constrained by weak/strong segment signals.

Using the evaluation setup across SQuADv2, Tweet Eval, XSUM, Common Gen, and GSM8K on GPT-3.5-Turbo and GPT-4o-mini, SAPO achieves the best average score against Zero-shot and strong APO baselines including APE, OPRO, Evo Prompt, GEPA, and StraGO. ar Xiv Labs is a framework that allows collaborators to develop and share new ar Xiv features directly on our website. Both individuals and organizations that work with ar Xiv Labs have embraced and accepted our values of openness, community, excellence, and user data privacy. ar Xiv is committed to these values and only works with partners that adhere to them.

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Sources cited: ๐Ÿ“ฐ ArXiv AI โ†—

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