环境建模的大型语言模型:框架,能力,约束
1Graduate School of Environmental Science, Hokkaido University, Sapporo, 060-0810, Japan.
Journal of environmental management
|December 31, 2025
概括
大型语言模型 (LLM) 为环境建模提供了新的途径. 人与人工智能的Copilot框架在参数校准和实时校正方面表现出色,而Autopilot则面临局限性.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 计算建模 计算建模
背景情况:
- 环境建模的复杂性正在增加.
- 将大型语言模型 (LLM) 集成到这些工作流中存在挑战.
- 需要在环境建模中整合LLM的实际框架.
研究的目的:
- 引入和评估将LLM嵌入环境建模工作流程的两个框架:一个人-AI协作Copilot和一个LLM驱动的Autopilot.
- 通过RRI模型评估这些框架在参数校准和实时校正方面的性能.
- 为环境建模中LLM的普遍化部署提供指导.
主要方法:
- 开发两种LLM集成框架:Copilot (人类-AI协作) 和Autopilot (LLM驱动的自动化).
- 将框架应用于日本库祖鲁河流域的降雨-排水-洪水 (RRI) 模型.
- 评估参数校准和实时校正任务的性能,利用快速工程和物理约束.
主要成果:
- 柯皮洛特框架表现出强大的性能,实现了参数校准的高精度 (NSE 0.91/0.81) 和稳定的实时校正.
- 自动驾驶框架在受物理约束的校准中表现出能力,但由于"注意力衰减",在长序实时校正中失败了.
- 在人类监督下,LLM作为知识引擎和编码助手是有效的 (Copilot),但完全自动化 (Autopilot) 受到上下文窗口限制的限制.
结论:
- 人与人工智能的协作框架 (Copilot) 目前在复杂的环境建模任务中比完全自动化的框架 (Autopilot) 更有效.
- 战略任务设计,人类监督,以及解决LLM诸如"注意力衰减"等局限性,对于成功的LLM整合至关重要.
- 该研究提供了一个方法框架和设计原则,用于在环境建模中部署LLMs,突出未来的研究方向.
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