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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 10

Puma-guided Pareto optimization for efficient multi-objective prompt generation

Dong Xiangqian1
Xiao Zheng2,3
1. Large Model Application & Research Center (LLMC), Chengdu Neusoft College, Chengdu 611844, China
2. School of Intelligent Manufacturing & Information Engineering, Sichuan Technology & Business College, Chengdu 611830, China
3. Guangdong Vocational College of Hotel Management, School of Information Engineering, Dongguan Guangdong 523960, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0074

Abstract

Existing prompt optimization methods suffer from limitations in scalability and adaptivity. To address these issues, this study developed a multi-objective prompt optimization framework, Puma-MOPT, based on the Puma algorithm. The framework integrates the adaptive phase switching and global search capabilities of the Puma algorithm with PromptWizard's prompt generation and evaluation mechanism to enable automatic prompt search and multi-objective trade-off. To improve search efficiency and enhance generalization in few-shot scenarios, Puma-MOPT incorporates a semantic similarity constraint and employs an adversarial filtering technique. Experimental results in five domains including mathematical reasoning, medical question answering, and code generation demonstrated that the framework significantly outperformed baseline methods such as NSGA-II, MOEA/D, EvoPrompt, and PromptWizard on multiple evaluation metrics. Puma-MOPT provides an efficient, robust, and general solution for Large Language Model (LLM) prompt engineering.

Foundation Support

2023年四川省科技厅重点研发项目(23ZDYF0647)
四川省教育发展研究中心课题(CJF23011)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0074
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 10

Publish History

[2025-06-04] Accepted Paper

Cite This Article

董祥千, 肖铮. 基于Puma算法引导帕累托前沿的高效多目标提示优化方法 [J]. 计算机应用研究, 2025, 42 (10). (2025-06-04). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0074. (Dong Xiangqian, Xiao Zheng. Puma-guided Pareto optimization for efficient multi-objective prompt generation [J]. Application Research of Computers, 2025, 42 (10). (2025-06-04). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0074. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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