ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models
This work addresses the problem of evaluating climate science models for researchers and practitioners, but it is incremental as it builds on existing evaluation methods in a specific domain.
The authors tackled the lack of a comprehensive evaluation framework for climate question answering models by developing ClimaQA, which includes expert-annotated and synthetic datasets, and compared different LLMs on these benchmarks to offer insights into enhancing climate knowledge.
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. The source code is publicly available at https://github.com/Rose-STL-Lab/genie-climaqa