EMPOWER: Evolutionary Medical Prompt Optimization With Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912552067268608 |
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| author | Chen, Yinda He, Yangfan Yang, Jing Zhang, Dapeng Yuan, Zhenlong Khan, Muhammad Attique Baili, Jamel Yee, Por Lip |
| author_facet | Chen, Yinda He, Yangfan Yang, Jing Zhang, Dapeng Yuan, Zhenlong Khan, Muhammad Attique Baili, Jamel Yee, Por Lip |
| contents | Prompt engineering significantly influences the reliability and clinical utility of Large Language Models (LLMs) in medical applications. Current optimization approaches inadequately address domain-specific medical knowledge and safety requirements. This paper introduces EMPOWER, a novel evolutionary framework that enhances medical prompt quality through specialized representation learning, multi-dimensional evaluation, and structure-preserving algorithms. Our methodology incorporates: (1) a medical terminology attention mechanism, (2) a comprehensive assessment architecture evaluating clarity, specificity, clinical relevance, and factual accuracy, (3) a component-level evolutionary algorithm preserving clinical reasoning integrity, and (4) a semantic verification module ensuring adherence to medical knowledge. Evaluation across diagnostic, therapeutic, and educational tasks demonstrates significant improvements: 24.7% reduction in factually incorrect content, 19.6% enhancement in domain specificity, and 15.3% higher clinician preference in blinded evaluations. The framework addresses critical challenges in developing clinically appropriate prompts, facilitating more responsible integration of LLMs into healthcare settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EMPOWER: Evolutionary Medical Prompt Optimization With Reinforcement Learning Chen, Yinda He, Yangfan Yang, Jing Zhang, Dapeng Yuan, Zhenlong Khan, Muhammad Attique Baili, Jamel Yee, Por Lip Computation and Language Prompt engineering significantly influences the reliability and clinical utility of Large Language Models (LLMs) in medical applications. Current optimization approaches inadequately address domain-specific medical knowledge and safety requirements. This paper introduces EMPOWER, a novel evolutionary framework that enhances medical prompt quality through specialized representation learning, multi-dimensional evaluation, and structure-preserving algorithms. Our methodology incorporates: (1) a medical terminology attention mechanism, (2) a comprehensive assessment architecture evaluating clarity, specificity, clinical relevance, and factual accuracy, (3) a component-level evolutionary algorithm preserving clinical reasoning integrity, and (4) a semantic verification module ensuring adherence to medical knowledge. Evaluation across diagnostic, therapeutic, and educational tasks demonstrates significant improvements: 24.7% reduction in factually incorrect content, 19.6% enhancement in domain specificity, and 15.3% higher clinician preference in blinded evaluations. The framework addresses critical challenges in developing clinically appropriate prompts, facilitating more responsible integration of LLMs into healthcare settings. |
| title | EMPOWER: Evolutionary Medical Prompt Optimization With Reinforcement Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.17703 |