ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models
Veeramakali Vignesh Manivannan, Yasaman Jafari, Srikar Eranky, Spencer Ho, Rose Yu, Duncan Watson-Parris, Yian Ma, Leon Bergen
Abstract
The use of Large Language Models (LLMs) in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs. To address this issue, we develop *ClimaGen* (Climate QA Generator), an adaptive learning framework that generates question-answer pairs from graduate textbooks with climate scientists in the loop. As a result, we present *ClimaQA-Gold*, an expert-annotated benchmark dataset alongside *ClimaQA-Silver*, a large-scale, comprehensive synthetic QA dataset for climate science. Finally, we develop evaluation strategies and compare different LLMs on our benchmarks. Our results offer novel insights into various approaches used to enhance knowledge of climate LLMs. ClimaQA’s source code is publicly available at https://github.com/Rose-STL-Lab/genie-climaqa
BibTeX
@inproceedings{
manivannan2025climaqa,
title={Clima{QA}: An Automated Evaluation Framework for Climate Question Answering Models},
author={Veeramakali Vignesh Manivannan and Yasaman Jafari and Srikar Eranky and Spencer Ho and Rose Yu and Duncan Watson-Parris and Yian Ma and Leon Bergen and Taylor Berg-Kirkpatrick},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=goFpCuJalN}
}