ChatClimate: Grounding Conversational AI in Climate Science

Saeid Vaghefi, Qian Wang, Veruska Muccione, Jingwei Ni, Mathias Kraus, Julia Bingler, Tobias Schimanski, Chiara Colesanti Senni, Nicolas Webersinke, Christian Huggel and Markus Leippold
Additional contact information
Saeid Vaghefi: University of Zurich
Qian Wang: University of Zurich; Inovest Partners AG
Veruska Muccione: University of Zurich; University of Geneva
Jingwei Ni: ETH Zurich
Mathias Kraus: University of Erlangen
Julia Bingler: University of Oxford
Tobias Schimanski: University of Zurich
Chiara Colesanti Senni: ETH Zurich; University of Zurich
Nicolas Webersinke: Friedrich-Alexander-Universität Erlangen-Nürnberg
Christian Huggel: University of Zurich
Markus Leippold: University of Zurich; Swiss Finance Institute

No 23-88, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-to-date information from reliable sources in a limited time is essential and difficult. To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory) to continuously update their knowledge and prevent the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by integrating the information from the Sixth Assessment Report of the Intergovernmental (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain. We present our conversational AI prototype, available at www.chatclimate.ai, for his invaluable and voluntary support in setting up the server. The server will become available by mid-April.} and demonstrate its ability to answer challenging questions accurately. The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high). The evaluation showed that the hybrid chatClimate provided more accurate answers, highlighting the effectiveness of our solution. This approach can be easily scaled for chatbots in specific domains, enabling the delivery of reliable and accurate information.

Pages: 22 pages
Date: 2023-10
New Economics Papers: this item is included in nep-ain, nep-cmp and nep-env
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