通过多代理的LLM系统加速地球科学发现
Dmitrii Pantiukhin1, Boris Shapkin1, Ivan Kuznetsov1
1Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany.
Frontiers in artificial intelligence
|November 28, 2025
概括
由大型语言模型 (LLM) 驱动的多代理系统 (MAS) 可以通过提高数据可访问性和加速科学发现来彻底改变地球科学. 这种方法为复杂的地球和环境科学数据集提供智能数据处理和自然语言接口.
科学领域:
- 地质科学 地质科学
- 地球和环境科学 地球和环境科学
- 数据科学数据科学数据科学
背景情况:
- 地理科学数据库面临复杂的数据格式,不一致的元数据和大量未经处理的数据的挑战.
- 科学家们努力有效地与各种地球和环境科学数据集进行交互和利用.
- 当前的数据管理实践阻碍了地球科学领域的可访问性和跨学科合作.
研究的目的:
- 探索与大型语言模型 (LLM) 集成的多代理系统 (MAS) 对地质科学数据的变革潜力.
- 展示MAS如何增强科学家与复杂地质科学数据的互动.
- 建议MAS在地球科学数据处理中的未来方向.
主要方法:
- 由大型语言模型 (LLM) 驱动的多代理系统 (MAS) 的概念探索.
- 一个专门的MAS管道的插图",PANGAEA GPT",与PANGAEA数据库集成.
- 讨论当前的地质科学数据挑战和相关科学领域的进展.
主要成果:
- 马斯提供智能数据处理,自然语言接口和合作解决地质科学数据的问题.
- "PANGAEA GPT"展示了PANGAEA数据库中复杂数据集的有效管理.
- MAS驱动的工作流可以显著加速地球和环境科学的科学发现.
结论:
- 具有大型语言模型 (LLM) 的多代理系统 (MAS) 可以从根本上改善地质科学数据的可访问性.
- 这种方法促进了跨学科的合作,加速了地球科学领域的科学发现.
- 将MAS集成到地球科学数据处理管道中是一个有希望的未来方向.
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