GeoGPT-RAG Technical Report

• URL: http://arxiv.org/abs/2509.09686v2
• Date: Mon, 15 Sep 2025 01:00:13 GMT
• Title: GeoGPT-RAG Technical Report
• Authors: Fei Huang, Fan Wu, Zeqing Zhang, Qihao Wang, Long Zhang, Grant Michael Boquet, Hongyang Chen,
• Abstract summary: GeoGPT is an open large language model system built to advance research in the geosciences.<n>RAG augments model outputs with relevant information retrieved from an external knowledge source.<n>RAG uses RAG to draw from the GeoGPT Library, a specialized corpus curated for geoscientific content.
• Score: 48.23789135946953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GeoGPT is an open large language model system built to advance research in the geosciences. To enhance its domain-specific capabilities, we integrated Retrieval Augmented Generation(RAG), which augments model outputs with relevant information retrieved from an external knowledge source. GeoGPT uses RAG to draw from the GeoGPT Library, a specialized corpus curated for geoscientific content, enabling it to generate accurate, context-specific answers. Users can also create personalized knowledge bases by uploading their own publication lists, allowing GeoGPT to retrieve and respond using user-provided materials. To further improve retrieval quality and domain alignment, we fine-tuned both the embedding model and a ranking model that scores retrieved passages by relevance to the query. These enhancements optimize RAG for geoscience applications and significantly improve the system's ability to deliver precise and trustworthy outputs. GeoGPT reflects a strong commitment to open science through its emphasis on collaboration, transparency, and community driven development. As part of this commitment, we have open-sourced two core RAG components-GeoEmbedding and GeoReranker-to support geoscientists, researchers, and professionals worldwide with powerful, accessible AI tools.

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