Enhancing Large Language Models with Climate Resources

Mathias Kraus, Julia Bingler, Markus Leippold, Tobias Schimanski, Chiara Colesanti Senni, Dominik Stammbach, Saeid Vaghefi and Nicolas Webersinke
Additional contact information
Mathias Kraus: University of Erlangen-Nuremberg
Julia Bingler: University of Oxford
Markus Leippold: University of Zurich; Swiss Finance Institute
Tobias Schimanski: University of Zurich
Chiara Colesanti Senni: ETH Zürich; University of Zurich
Dominik Stammbach: ETH Zürich
Saeid Vaghefi: University of Zurich
Nicolas Webersinke: Friedrich-Alexander-Universität Erlangen-Nürnberg

No 23-99, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability to generate human-like text across diverse topics. However, despite their impressive capabilities, LLMs lack recent information and often employ imprecise language, which can be detrimental in domains where accuracy is crucial, such as climate change. In this study, we make use of recent ideas to harness the potential of LLMs by viewing them as agents that access multiple sources, including databases containing recent and precise information about organizations, institutions, and companies. We demonstrate the effectiveness of our method through a prototype agent that retrieves emission data from ClimateWatch (https://www.climatewatchdata.org/) and leverages general Google search. By integrating these resources with LLMs, our approach overcomes the limitations associated with imprecise language and delivers more reliable and accurate information in the critical domain of climate change. This work paves the way for future advancements in LLMs and their application in domains where precision is of paramount importance.

Pages: 7 pages
Date: 2023-10
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2399

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