Assessing Large Language Models on Climate Information

• URL: http://arxiv.org/abs/2310.02932v2
• Date: Tue, 28 May 2024 15:36:49 GMT
• Title: Assessing Large Language Models on Climate Information
• Authors: Jannis Bulian, Mike S. Schäfer, Afra Amini, Heidi Lam, Massimiliano Ciaramita, Ben Gaiarin, Michelle Chen Hübscher, Christian Buck, Niels G. Mede, Markus Leippold, Nadine Strauß,
• Abstract summary: We present a comprehensive evaluation framework grounded in science communication research to assess Large Language Models (LLMs) Our framework emphasizes both presentational responses and adequacy, offering a fine-grained analysis of LLM generations spanning 8 dimensions and 30 issues. We introduce a novel protocol for scalable oversight that relies on AI Assistance and raters with relevant education.
• Score: 5.034118180129635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains. We present a comprehensive evaluation framework, grounded in science communication research, to assess LLM responses to questions about climate change. Our framework emphasizes both presentational and epistemological adequacy, offering a fine-grained analysis of LLM generations spanning 8 dimensions and 30 issues. Our evaluation task is a real-world example of a growing number of challenging problems where AI can complement and lift human performance. We introduce a novel protocol for scalable oversight that relies on AI Assistance and raters with relevant education. We evaluate several recent LLMs on a set of diverse climate questions. Our results point to a significant gap between surface and epistemological qualities of LLMs in the realm of climate communication.

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