用多智能体大模型提升地球科学数据处理效率
Accelerating Earth Science Discovery via Multi-Agent LLM Systems
- 构建基于LLM的多智能体系统，实现自然语言操作地质数据
- 在PANGAEA数据库上验证，显著降低数据处理门槛
- 适合跨学科科研人员快速获取和分析地球科学数据
本文探讨了由大型语言模型驱动的多智能体系统（MAS）在地球科学领域的变革潜力。地球科学数据仓库用户面临数据格式复杂多样、元数据标准不一及大量未处理数据等挑战。多智能体系统通过智能数据处理、自然语言交互和协作求解能力，可显著改善科学家与数据的互动方式。文中以集成于地球与环境科学数据库PANGAEA的专用系统「PANGAEA GPT」为例，展示了多智能体工作流在管理复杂数据集方面的有效性，加速了科学发现进程。本文还讨论了多智能体如何应对当前地球科学数据难题，回顾了其他科学领域相关进展，并提出未来将多智能体融入地球科学数据处理流程的方向。结果表明，多智能体系统能从根本上提升数据可及性，促进跨学科合作，加快地球科学研究速度。
原文摘要 · Abstract (English)
This Perspective explores the transformative potential of Multi-Agent Systems (MAS) powered by Large Language Models (LLMs) in the geosciences. Users of geoscientific data repositories face challenges due to the complexity and diversity of data formats, inconsistent metadata practices, and a considerable number of unprocessed datasets. MAS possesses transformative potential for improving scientists' interaction with geoscientific data by enabling intelligent data processing, natural language interfaces, and collaborative problem-solving capabilities. We illustrate this approach with "PANGAEA GPT", a specialized MAS pipeline integrated with the diverse PANGAEA database for Earth and Environmental Science, demonstrating how MAS-driven workflows can effectively manage complex datasets and accelerate scientific discovery. We discuss how MAS can address current data challenges in geosciences, highlight advancements in other scientific fields, and propose future directions for integrating MAS into geoscientific data processing pipelines. In this Perspective, we show how MAS can fundamentally improve data accessibility, promote cross-disciplinary collaboration, and accelerate geoscientific discoveries.
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