arXiv:2606.10194v1 Announce Type: new 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 v
The increasing complexity of climate change research demands more sophisticated AI tools, coinciding with advancements in multimodal AI capabilities that can process diverse data types.
This development addresses a critical gap in climate science AI, enabling more comprehensive and accurate analysis of complex climate data, which is essential for effective mitigation and adaptation strategies.
AI systems can now better incorporate and reason across text, visual content, and scientific figures in climate science, moving beyond predominantly textual analyses.
- · Climate scientists
- · AI researchers in multimodal learning
- · Organizations focused on climate modeling and prediction
- · Developers of unimodal climate AI solutions
- · Organizations reliant on siloed climate data analysis methods
Improved accuracy and breadth of climate change assessments due to better AI integration of various data sources.
Accelerated development of AI-driven climate solutions and policy recommendations capable of handling nuanced, real-world data.
Enhanced public and private sector investment in AI for climate science, potentially leading to more effective climate action and resource allocation.
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Read at arXiv cs.LG