Ocean science, which delves into the oceans that are reservoirs of life and
biodiversity, is of great significance given that oceans cover over 70% of our
planet's surface. Recently, advances in Large Language Models (LLMs) have
transformed the paradigm in science. Despite the success in other domains,
current LLMs often fall short in catering to the needs of domain experts like
oceanographers, and the potential of LLMs for ocean science is under-explored.
The intrinsic reason may be the immense and intricate nature of ocean data as
well as the necessity for higher granularity and richness in knowledge. To
alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean
domain, which is expert in various ocean science tasks. We propose DoInstruct,
a novel framework to automatically obtain a large volume of ocean domain
instruction data, which generates instructions based on multi-agent
collaboration. Additionally, we construct the first oceanography benchmark,
OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though
comprehensive experiments, OceanGPT not only shows a higher level of knowledge
expertise for oceans science tasks but also gains preliminary embodied
intelligence capabilities in ocean technology. Codes, data and checkpoints will
soon be available at https://github.com/zjunlp/KnowLM.Comment: Work in progress. Project Website:
https://zjunlp.github.io/project/OceanGPT