Large language models (LLMs)have achieved great success in general domains of
natural language processing. In this paper, we bring LLMs to the realm of
geoscience, with the objective of advancing research and applications in this
field. To this end, we present the first-ever LLM in geoscience, K2, alongside
a suite of resources developed to further promote LLM research within
geoscience. For instance, we have curated the first geoscience instruction
tuning dataset, GeoSignal, which aims to align LLM responses to
geoscience-related user queries. Additionally, we have established the first
geoscience benchmark, GeoBenchmark, to evaluate LLMs in the context of
geoscience. In this work, we experiment with a complete recipe to adapt a
pretrained general-domain LLM to the geoscience domain. Specifically, we
further train the LLaMA-7B model on over 1 million pieces of geoscience
literature and utilize GeoSignal's supervised data to fine-tune the model.
Moreover, we share a protocol that can efficiently gather domain-specific data
and construct domain-supervised data, even in situations where manpower is
scarce. Experiments conducted on the GeoBenchmark demonstrate the the
effectiveness of our approach and datasets