Enhancing Large Language Models with Climate Resources

• URL: http://arxiv.org/abs/2304.00116v1
• Date: Fri, 31 Mar 2023 20:24:14 GMT
• Title: Enhancing Large Language Models with Climate Resources
• Authors: Mathias Kraus, Julia Anna Bingler, Markus Leippold, Tobias Schimanski, Chiara Colesanti Senni, Dominik Stammbach, Saeid Ashraf Vaghefi, Nicolas Webersinke
• Abstract summary: Large language models (LLMs) have transformed the landscape of artificial intelligence by demonstrating their ability in generating human-like text. However, they 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. We demonstrate the effectiveness of our method through a prototype agent that retrieves emission data from ClimateWatch.
• Score: 5.2677629053588895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability in generating 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.

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