A Hybrid GA LLM Framework for Structured Task Optimization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916794787168256 |
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| author | Shum, William Chan, Rachel Lin, Jonas Feng, Benny Lau, Patrick |
| author_facet | Shum, William Chan, Rachel Lin, Jonas Feng, Benny Lau, Patrick |
| contents | GA LLM is a hybrid framework that combines Genetic Algorithms with Large Language Models to handle structured generation tasks under strict constraints. Each output, such as a plan or report, is treated as a gene, and evolutionary operations like selection, crossover, and mutation are guided by the language model to iteratively improve solutions. The language model provides domain knowledge and creative variation, while the genetic algorithm ensures structural integrity and global optimization. GA LLM has proven effective in tasks such as itinerary planning, academic outlining, and business reporting, consistently producing well structured and requirement satisfying results. Its modular design also makes it easy to adapt to new tasks. Compared to using a language model alone, GA LLM achieves better constraint satisfaction and higher quality solutions by combining the strengths of both components. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_07483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hybrid GA LLM Framework for Structured Task Optimization Shum, William Chan, Rachel Lin, Jonas Feng, Benny Lau, Patrick Computation and Language GA LLM is a hybrid framework that combines Genetic Algorithms with Large Language Models to handle structured generation tasks under strict constraints. Each output, such as a plan or report, is treated as a gene, and evolutionary operations like selection, crossover, and mutation are guided by the language model to iteratively improve solutions. The language model provides domain knowledge and creative variation, while the genetic algorithm ensures structural integrity and global optimization. GA LLM has proven effective in tasks such as itinerary planning, academic outlining, and business reporting, consistently producing well structured and requirement satisfying results. Its modular design also makes it easy to adapt to new tasks. Compared to using a language model alone, GA LLM achieves better constraint satisfaction and higher quality solutions by combining the strengths of both components. |
| title | A Hybrid GA LLM Framework for Structured Task Optimization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.07483 |