A Toolbox for Improving Evolutionary Prompt Search
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908635840380928 |
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| author | Grießhaber, Daniel Kimmich, Maximilian Maucher, Johannes Vu, Ngoc Thang |
| author_facet | Grießhaber, Daniel Kimmich, Maximilian Maucher, Johannes Vu, Ngoc Thang |
| contents | Evolutionary prompt optimization has demonstrated effectiveness in refining prompts for LLMs. However, existing approaches lack robust operators and efficient evaluation mechanisms. In this work, we propose several key improvements to evolutionary prompt optimization that can partially generalize to prompt optimization in general: 1) decomposing evolution into distinct steps to enhance the evolution and its control, 2) introducing an LLM-based judge to verify the evolutions, 3) integrating human feedback to refine the evolutionary operator, and 4) developing more efficient evaluation strategies that maintain performance while reducing computational overhead. Our approach improves both optimization quality and efficiency. We release our code, enabling prompt optimization on new tasks and facilitating further research in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Toolbox for Improving Evolutionary Prompt Search Grießhaber, Daniel Kimmich, Maximilian Maucher, Johannes Vu, Ngoc Thang Computation and Language Evolutionary prompt optimization has demonstrated effectiveness in refining prompts for LLMs. However, existing approaches lack robust operators and efficient evaluation mechanisms. In this work, we propose several key improvements to evolutionary prompt optimization that can partially generalize to prompt optimization in general: 1) decomposing evolution into distinct steps to enhance the evolution and its control, 2) introducing an LLM-based judge to verify the evolutions, 3) integrating human feedback to refine the evolutionary operator, and 4) developing more efficient evaluation strategies that maintain performance while reducing computational overhead. Our approach improves both optimization quality and efficiency. We release our code, enabling prompt optimization on new tasks and facilitating further research in this area. |
| title | A Toolbox for Improving Evolutionary Prompt Search |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.05120 |