A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908446988697600 |
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| author | Cui, Wendi Li, Zhuohang Sun, Hao Lopez, Damien Das, Kamalika Malin, Bradley A. Kumar, Sricharan Zhang, Jiaxin |
| author_facet | Cui, Wendi Li, Zhuohang Sun, Hao Lopez, Damien Das, Kamalika Malin, Bradley A. Kumar, Sricharan Zhang, Jiaxin |
| contents | Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt optimization employing heuristic-based search algorithms can systematically explore and improve prompts with minimal human oversight. This survey proposes a comprehensive taxonomy of these methods, categorizing them by where optimization occurs, what is optimized, what criteria drive the optimization, which operators generate new prompts, and which iterative search algorithms are applied. We further highlight specialized datasets and tools that support and accelerate automated prompt refinement. We conclude by discussing key open challenges pointing toward future opportunities for more robust and versatile LLM applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm Cui, Wendi Li, Zhuohang Sun, Hao Lopez, Damien Das, Kamalika Malin, Bradley A. Kumar, Sricharan Zhang, Jiaxin Computation and Language Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt optimization employing heuristic-based search algorithms can systematically explore and improve prompts with minimal human oversight. This survey proposes a comprehensive taxonomy of these methods, categorizing them by where optimization occurs, what is optimized, what criteria drive the optimization, which operators generate new prompts, and which iterative search algorithms are applied. We further highlight specialized datasets and tools that support and accelerate automated prompt refinement. We conclude by discussing key open challenges pointing toward future opportunities for more robust and versatile LLM applications. |
| title | A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.18746 |