MMClima: A Framework for Multimodal Climate Science Data and Evaluation

Abstract:Climate change research increasingly requires AI systems that reason across text, dynamic visual content, and scientific figures, yet existing climate QA benchmarks are small, mostly textual, and cover a narrow range of models. We introduce MMClima, a large-scale multimodal climate question answering framework with 104k+ expert-validated question-answer pairs spanning articles, video transcriptions, and figures across five core climate science domains. MMClima is constructed via automated claim extraction and QA synthesis with human-in-the-loop validation to ensure both scale and reliability. Using MMClima, we benchmark state-of-the-art multimodal language models on tasks requiring factual recall, visual interpretation, and cross-modal synthesis. We additionally fine-tune on the textual split to produce mmclima-70b-txt, a domain-adapted baseline that outperforms strong open- and closed-source models on textual QA. We release the dataset, evaluation pipeline, fine-tuned model weights, and data creation framework to support standardized multimodal evaluation for climate science.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.10194 [cs.LG]

(or arXiv:2606.10194v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.10194 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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