arXiv:2605.24844v1 Announce Type: cross Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS. To bridge this gap, we introduce Geo-Expert, a family of parameter-efficient geological LLMs fine-tuned on a custom-curated, high-quality instruction dataset processed using our custom instruction synthesis pipeline. We investigate the impact of model scaling and architecture by fine-tuning three base models:
The proliferation of general-purpose LLMs has highlighted their limitations in specialized domains like geology, prompting a focused effort to create more accurate, domain-specific AI solutions.
This development indicates a crucial step towards highly specialized AI applications, moving beyond general LLMs to address specific industry needs with expert-level reasoning, crucial for sectors like energy and resource extraction.
The ability of AI to perform expert-level geological reasoning will shift how exploration and analysis are conducted, reducing reliance on conventional methods and potentially accelerating discoveries.
- · Geological survey companies
- · Oil and gas industry
- · Mining sector
- · AI model developers
- · Conventional geological consulting firms
- · Entry-level geological data analysts
Specialized AI models like Geo-Expert begin to outperform general LLMs in specific scientific and industrial fields.
Increased efficiency and accuracy in geological analysis lead to more effective resource exploration and reduced operational costs.
The success of domain-specific LLMs spurs further investment and development in highly specialized AI applications across various scientific and engineering disciplines.
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Read at arXiv cs.CL