Rethinking Prompt Optimizers: From Prompt Merits to Optimization

• URL: http://arxiv.org/abs/2505.09930v2
• Date: Tue, 20 May 2025 11:48:05 GMT
• Title: Rethinking Prompt Optimizers: From Prompt Merits to Optimization
• Authors: Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Mak Lee Onn, Gee Wah Ng, Kezhi Mao,
• Abstract summary: We introduce MePO, a merit-guided, lightweight, locally deployable prompt training dataset built from merit-aligned prompts.<n>MePO avoids online optimization, reduces cost and privacy concerns, and, by learning clear, interpretable merits, generalizes effectively to both large-scale and lightweight inference models.
• Score: 14.01541576309104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt optimization (PO) provides a practical way to improve response quality when users lack the time or expertise to manually craft effective prompts. Existing methods typically rely on advanced, large-scale LLMs like GPT-4 to generate optimized prompts. However, due to limited downward compatibility, verbose, instruction-heavy prompts from advanced LLMs can overwhelm lightweight inference models and degrade response quality. In this work, we rethink prompt optimization through the lens of interpretable design. We first identify a set of model-agnostic prompt quality merits and empirically validate their effectiveness in enhancing prompt and response quality. We then introduce MePO, a merit-guided, lightweight, and locally deployable prompt optimizer trained on our preference dataset built from merit-aligned prompts generated by a lightweight LLM. Unlike prior work, MePO avoids online optimization reliance, reduces cost and privacy concerns, and, by learning clear, interpretable merits, generalizes effectively to both large-scale and lightweight inference models. Experiments demonstrate that MePO achieves better results across diverse tasks and model types, offering a scalable and robust solution for real-world deployment. The code and dataset can be found in https://github.com/MidiyaZhu/MePO

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