@inproceedings{li-etal-2024-using-llms,
title = "Using {LLM}s to Build a Database of Climate Extreme Impacts",
author = {Li, Ni and
Zahra, Shorouq and
Brito, Mariana and
Flynn, Clare and
G{\"o}rnerup, Olof and
Worou, Koffi and
Kurfali, Murathan and
Meng, Chanjuan and
Thiery, Wim and
Zscheischler, Jakob and
Messori, Gabriele and
Nivre, Joakim},
editor = "Stammbach, Dominik and
Ni, Jingwei and
Schimanski, Tobias and
Dutia, Kalyan and
Singh, Alok and
Bingler, Julia and
Christiaen, Christophe and
Kushwaha, Neetu and
Muccione, Veruska and
A. Vaghefi, Saeid and
Leippold, Markus",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.climatenlp-1.7",
pages = "93--110",
abstract = "To better understand how extreme climate events impact society, we need to increase the availability of accurate and comprehensive information about these impacts. We propose a method for building large-scale databases of climate extreme impacts from online textual sources, using LLMs for information extraction in combination with more traditional NLP techniques to improve accuracy and consistency. We evaluate the method against a small benchmark database created by human experts and find that extraction accuracy varies for different types of information. We compare three different LLMs and find that, while the commercial GPT-4 model gives the best performance overall, the open-source models Mistral and Mixtral are competitive for some types of information.",
}
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<abstract>To better understand how extreme climate events impact society, we need to increase the availability of accurate and comprehensive information about these impacts. We propose a method for building large-scale databases of climate extreme impacts from online textual sources, using LLMs for information extraction in combination with more traditional NLP techniques to improve accuracy and consistency. We evaluate the method against a small benchmark database created by human experts and find that extraction accuracy varies for different types of information. We compare three different LLMs and find that, while the commercial GPT-4 model gives the best performance overall, the open-source models Mistral and Mixtral are competitive for some types of information.</abstract>
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<date>2024-08</date>
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%0 Conference Proceedings
%T Using LLMs to Build a Database of Climate Extreme Impacts
%A Li, Ni
%A Zahra, Shorouq
%A Brito, Mariana
%A Flynn, Clare
%A Görnerup, Olof
%A Worou, Koffi
%A Kurfali, Murathan
%A Meng, Chanjuan
%A Thiery, Wim
%A Zscheischler, Jakob
%A Messori, Gabriele
%A Nivre, Joakim
%Y Stammbach, Dominik
%Y Ni, Jingwei
%Y Schimanski, Tobias
%Y Dutia, Kalyan
%Y Singh, Alok
%Y Bingler, Julia
%Y Christiaen, Christophe
%Y Kushwaha, Neetu
%Y Muccione, Veruska
%Y A. Vaghefi, Saeid
%Y Leippold, Markus
%S Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-etal-2024-using-llms
%X To better understand how extreme climate events impact society, we need to increase the availability of accurate and comprehensive information about these impacts. We propose a method for building large-scale databases of climate extreme impacts from online textual sources, using LLMs for information extraction in combination with more traditional NLP techniques to improve accuracy and consistency. We evaluate the method against a small benchmark database created by human experts and find that extraction accuracy varies for different types of information. We compare three different LLMs and find that, while the commercial GPT-4 model gives the best performance overall, the open-source models Mistral and Mixtral are competitive for some types of information.
%U https://aclanthology.org/2024.climatenlp-1.7
%P 93-110
Markdown (Informal)
[Using LLMs to Build a Database of Climate Extreme Impacts](https://aclanthology.org/2024.climatenlp-1.7) (Li et al., ClimateNLP-WS 2024)
ACL
- Ni Li, Shorouq Zahra, Mariana Brito, Clare Flynn, Olof Görnerup, Koffi Worou, Murathan Kurfali, Chanjuan Meng, Wim Thiery, Jakob Zscheischler, Gabriele Messori, and Joakim Nivre. 2024. Using LLMs to Build a Database of Climate Extreme Impacts. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), pages 93–110, Bangkok, Thailand. Association for Computational Linguistics.