ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change

• URL: http://arxiv.org/abs/2401.09646v1
• Date: Wed, 17 Jan 2024 23:29:46 GMT
• Title: ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
• Authors: David Thulke and Yingbo Gao and Petrus Pelser and Rein Brune and Rricha Jalota and Floris Fok and Michael Ramos and Ian van Wyk and Abdallah Nasir and Hayden Goldstein and Taylor Tragemann and Katie Nguyen and Ariana Fowler and Andrew Stanco and Jon Gabriel and Jordan Taylor and Dean Moro and Evgenii Tsymbalov and Juliette de Waal and Evgeny Matusov and Mudar Yaghi and Mohammad Shihadah and Hermann Ney and Christian Dugast and Jonathan Dotan and Daniel Erasmus
• Abstract summary: This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models from scratch on a science-oriented dataset of 300B tokens. ClimateGPT-7B, 13B and 70B are continuously pre-trained from Llama2 on a domain-specific dataset of 4.2B tokens.
• Score: 21.827936253363603
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models from scratch on a science-oriented dataset of 300B tokens. For the first model, the 4.2B domain-specific tokens were included during pre-training and the second was adapted to the climate domain after pre-training. Additionally, ClimateGPT-7B, 13B and 70B are continuously pre-trained from Llama~2 on a domain-specific dataset of 4.2B tokens. Each model is instruction fine-tuned on a high-quality and human-generated domain-specific dataset that has been created in close cooperation with climate scientists. To reduce the number of hallucinations, we optimize the model for retrieval augmentation and propose a hierarchical retrieval strategy. To increase the accessibility of our model to non-English speakers, we propose to make use of cascaded machine translation and show that this approach can perform comparably to natively multilingual models while being easier to scale to a large number of languages. Further, to address the intrinsic interdisciplinary aspect of climate change we consider different research perspectives. Therefore, the model can produce in-depth answers focusing on different perspectives in addition to an overall answer. We propose a suite of automatic climate-specific benchmarks to evaluate LLMs. On these benchmarks, ClimateGPT-7B performs on par with the ten times larger Llama-2-70B Chat model while not degrading results on general domain benchmarks. Our human evaluation confirms the trends we saw in our benchmarks. All models were trained and evaluated using renewable energy and are released publicly.

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