Go to ACL ARR 2026 January homepageMSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs
Keywords: Multimodal, Earth Science, Dataset, Benchmark
Abstract: The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application in earth science—especially at the graduate level—remains underexplored due to a lack of benchmarks reflecting the depth and complexity of geoscientific reasoning. Existing datasets often rely on synthetic data or simple figure-caption pairs, failing to capture the nuanced reasoning required for real-world applications. To address this, we introduce MSEarth, a multimodal scientific dataset and benchmark curated from high-quality, open-access publications. Covering the five major spheres of Earth science—atmosphere, cryosphere, hydrosphere, lithosphere, and biosphere—MSEarth features over 289K figures with refined captions enriched by contextual discussions and reasoning from the original papers. The benchmark supports tasks such as scientific figure captioning, multiple choice questions, and open-ended reasoning, providing a scalable, high-fidelity resource for developing and evaluating MLLMs in scientific reasoning.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: corpus creation, benchmarking, automatic creation and evaluation of language resources
Contribution Types: Publicly available software and/or pre-trained models, Data resources
Languages Studied: English
Submission Number: 5793
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