Chain-of-programming (CoP): empowering large language models for geospatial code generation task

doi:10.1080/17538947.2025.2509812

2025-05-27

DOI：10.1080/17538947.2025.2509812

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摘要

En 中文

大型语言模型（LLMs）推动了地理空间代码生成的开发，但面临用户需求不完整和平台特定语法知识不足等挑战，常导致“代码幻觉”。为解决此问题，本文提出了链式编程（CoP）框架，将生成过程分为五个步骤：需求分析、算法设计、代码实现、调试和注释。该框架整合了共享信息池、知识库检索和用户反馈机制，实现端到端代码生成，无需模型微调。基于地理空间问题分类和评估基准，CoP显著提升了生成代码的逻辑清晰度、语法正确性和可执行性，性能提升幅度达3.0%至48.8%。对比实验和消融实验进一步验证了CoP的优势及其核心组件的必要性。建筑可视化和火灾数据分析案例研究展示了该框架的实用价值。本研究还开发了并开源了一个原型系统，为基于LLMs的地理空间代码生成提供系统化、分步骤的解决方案，并为其他专业领域的代码生成提供了有价值的见解。

Keyword:

Geospatial code generation

Large language models

Code hallucination

Geographic information science

Google earth engine

期刊

International Journal of Digital Earth

IF:

4.9

论文数:

2.0K

被引数:

4.7K

机构

wuhan university

学者数:

8.1W

论文数: 5.8W

被引数: 70

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