OceanGPT: A Large Language Model for Ocean Science Tasks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914932743733248 |
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| author | Bi, Zhen Zhang, Ningyu Xue, Yida Ou, Yixin Ji, Daxiong Zheng, Guozhou Chen, Huajun |
| author_facet | Bi, Zhen Zhang, Ningyu Xue, Yida Ou, Yixin Ji, Daxiong Zheng, Guozhou Chen, Huajun |
| contents | 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 reasons are 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 large language model in the ocean domain, which is expert in various ocean science tasks. We also propose OceanGPT, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02031 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | OceanGPT: A Large Language Model for Ocean Science Tasks Bi, Zhen Zhang, Ningyu Xue, Yida Ou, Yixin Ji, Daxiong Zheng, Guozhou Chen, Huajun Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Robotics 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 reasons are 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 large language model in the ocean domain, which is expert in various ocean science tasks. We also propose OceanGPT, 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. |
| title | OceanGPT: A Large Language Model for Ocean Science Tasks |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Robotics |
| url | https://arxiv.org/abs/2310.02031 |