Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning
The paper introduces Geo-Expert, a series of parameter-efficient geological language models designed to improve reasoning in geology. It highlights the limitations of general-purpose large language models in geological contexts and presents a fine-tuning approach using a custom dataset. The findings suggest that a domain-aligned 8B model can outperform larger generalist models in specialized geological reasoning tasks.
- ▪Geo-Expert is a family of geological language models fine-tuned for expert-level reasoning.
- ▪The models were evaluated on a new benchmark called Geo-Eval, demonstrating superior performance in geological tasks.
- ▪The optimized 8B model offers a competitive cost-performance ratio for practical deployment.
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Computer Science > Artificial Intelligence arXiv:2605.24844 (cs) [Submitted on 24 May 2026] Title:Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning Authors:Chenyou Guo, Zongqi Liu, Yizhou Zhang, Zhaorui Jiang, Ze Liu View a PDF of the paper titled Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning, by Chenyou Guo and 4 other authors View PDF HTML (experimental) 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.
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