학술논문

Assessing Large Language Models on Climate Information
Document Type
Working Paper
Source
Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
Subject
Computer Science - Computation and Language
Computer Science - Artificial Intelligence
Computer Science - Computers and Society
Computer Science - Machine Learning
Language
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.