OceanGPT: A Large Language Model for Ocean Science Tasks
Keywords: instruction generation, benchmark, ocean science, large language model
TL;DR: A large language model designed for ocean science tasks, complemented by a compilation of resources.
Abstract: Ocean science, which are reservoirs of life and biodiversity, is of paramount 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 notable 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 is 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 science 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 demonstrates a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes are in the supplementary materials and will be released soon.
Supplementary Material: zip
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 882
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