Assessing Large Language Models on Climate Information
This work addresses the need for reliable AI in critically relevant domains like climate change, though it is incremental as it applies existing evaluation methods to a new domain.
The paper tackled the problem of assessing Large Language Models' capability in climate communication by developing a comprehensive evaluation framework based on science communication research, and found a significant gap between surface and epistemological qualities in LLM responses to climate questions.
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.