Summary of Climategpt: Towards Ai Synthesizing Interdisciplinary Research on Climate Change, by 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
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
by David Thulke, Yingbo Gao, Petrus Pelser, Rein Brune, Rricha Jalota, Floris Fok, Michael Ramos, Ian van Wyk, Abdallah Nasir, Hayden Goldstein, Taylor Tragemann, Katie Nguyen, Ariana Fowler, Andrew Stanco, Jon Gabriel, Jordan Taylor, Dean Moro, Evgenii Tsymbalov, Juliette de Waal, Evgeny Matusov, Mudar Yaghi, Mohammad Shihadah, Hermann Ney, Christian Dugast, Jonathan Dotan, Daniel Erasmus
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary ClimateGPT is a family of large language models that specialize in climate change research, synthesizing interdisciplinary knowledge from 300 billion tokens of scientific data. Two models, ClimateGPT-7B, were pre-trained on this dataset, with the first model incorporating domain-specific information during training and the second adapting to the climate domain after pre-training. The models are further fine-tuned on high-quality datasets created in collaboration with climate scientists. To reduce hallucinations, the authors optimize the models for retrieval augmentation and propose a hierarchical retrieval strategy. To make the models more accessible, they suggest using cascaded machine translation, which can perform similarly to multilingual models while being easier to scale. The models can produce in-depth answers from different research perspectives, making them valuable tools for climate change research. Benchmarks are proposed to evaluate LLMs, and ClimateGPT-7B performs on par with a larger model while maintaining general domain performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ClimateGPT is a new kind of computer program that helps scientists understand climate change better. It’s like a super-smart assistant that can read lots of scientific information and provide answers based on different perspectives. The program was trained using special data created by climate experts and has been tested to make sure it’s accurate. One cool thing about ClimateGPT is that it can translate information into other languages, making it useful for people who don’t speak English. The authors also came up with a way to evaluate how well the program does its job, and they found that it performs just as well as a bigger program while still being easy to use. |
Keywords
* Artificial intelligence * Translation
