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 Taylor Berg-Kirkpatrick
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
Climate Benchmark, Scientific Foundation Models, Scientific Question Answering, Large Language Models, Automated QA generation
We develop ClimaGen, an automated framework for generating climate science QA datasets, and introduce ClimaQA-Gold and ClimaQA-Silver as benchmark datasets to evaluate and improve the performance of foundation models in climate science.