Plum: Prompt Learning using Metaheuristic

• URL: http://arxiv.org/abs/2311.08364v3
• Date: Sun, 30 Jun 2024 09:50:11 GMT
• Title: Plum: Prompt Learning using Metaheuristic
• Authors: Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, Kashun Shum, Renjie Pi, Jipeng Zhang, Tong Zhang,
• Abstract summary: We introduce metaheuristics, a branch of discrete non-visual optimization methods with over 100 options. Within our paradigm, we test six typical methods, demonstrating their effectiveness in white-box and black-box prompt learning. We show that these methods can be used to discover more human-understandable prompts, opening the door to a cornucopia of possibilities in prompt optimization.
• Score: 28.024094195968672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, have even revealed previously unknown reasoning capabilities within these models. However, the progress of discovering effective prompts has been slow, driving a desire for general prompt optimization methods. Unfortunately, few existing prompt learning methods satisfy the criteria of being truly "general", i.e., automatic, discrete, black-box, gradient-free, and interpretable all at once. In this paper, we introduce metaheuristics, a branch of discrete non-convex optimization methods with over 100 options, as a promising approach to prompt learning. Within our paradigm, we test six typical methods: hill climbing, simulated annealing, genetic algorithms with/without crossover, tabu search, and harmony search, demonstrating their effectiveness in white-box and black-box prompt learning. Furthermore, we show that these methods can be used to discover more human-understandable prompts that were previously unknown in both reasoning and image generation tasks, opening the door to a cornucopia of possibilities in prompt optimization. We release all the codes in \url{https://github.com/research4pan/Plum}.

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