Wenhe Sun
2024
Plum: Prompt Learning using Metaheuristics
Rui Pan
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Shuo Xing
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Shizhe Diao
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Wenhe Sun
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Xiang Liu
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KaShun Shum
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Jipeng Zhang
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Renjie Pi
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Tong Zhang
Findings of the Association for Computational Linguistics ACL 2024
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.
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Co-authors
- Rui Pan 1
- Shuo Xing 1
- Shizhe Diao 1
- Xiang Liu 1
- Kashun Shum 1
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