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Large language models (LLMs) have achieved great success in general domainsof natural language processing. In this paper, we bring LLMs to the realm ofgeoscience with the objective of advancing research and applications in thisfield. To this end, we present the first-ever LLM in geoscience, K2, alongsidea suite of resources developed to further promote LLM research withingeoscience. For instance, we have curated the first geoscience instructiontuning dataset, GeoSignal, which aims to align LLM responses togeoscience-related user queries. Additionally, we have established the firstgeoscience benchmark, GeoBench, to evaluate LLMs in the context of geoscience.In this work, we experiment with a complete recipe to adapt a pre-trainedgeneral-domain LLM to the geoscience domain. Specifically, we further train theLLaMA-7B model on 5.5B tokens of geoscience text corpus, including over 1million pieces of geoscience literature, and utilize GeoSignal's superviseddata to fine-tune the model. Moreover, we share a protocol that can efficientlygather domain-specific data and construct domain-supervised data, even insituations where manpower is scarce. Meanwhile, we equip K2 with the abilitiesof using tools to be a naive geoscience aide. Experiments conducted on theGeoBench demonstrate the effectiveness of our approach and datasets ongeoscience knowledge understanding and utilization.We open-source all thetraining data and K2 model checkpoints at https://github.com/davendw49/k2.

K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization