Introducing MAPO: Momentum-Aided Gradient Descent Prompt Optimization

• URL: http://arxiv.org/abs/2410.19499v3
• Date: Thu, 26 Jun 2025 18:40:26 GMT
• Title: Introducing MAPO: Momentum-Aided Gradient Descent Prompt Optimization
• Authors: Anthony Cui, Pranav Nandyalam, Andrew Rufail, Ethan Cheung, Aiden Lei, Kevin Zhu, Sean O'Brien,
• Abstract summary: Building on ProTeGi, MAPO uses positive natural language "gradients" and a momentum-based extension to refine prompts effectively.<n>MAPO achieves faster convergence time with fewer API calls and higher F1 scores than ProTeGi.
• Score: 2.750784330885499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Momentum-Aided Prompt Optimization (MAPO) enhances the efficiency and efficacy of prompt optimization for Large Language Models (LLMs). Building on ProTeGi, MAPO uses positive natural language "gradients" and a momentum-based extension to refine prompts effectively. By tracking gradient history, MAPO avoids local minima and oscillations. It also utilizes beam search and an Upper Confidence Bound (UCB) algorithm for balanced candidate expansion and selection. Benchmark testing shows that MAPO achieves faster convergence time with fewer API calls and higher F1 scores than ProTeGi, proving it as a robust and scalable solution for automated prompt engineering in LLMs.

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