@inproceedings{zhao-etal-2026-msearth,
title = "{MSE}arth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with {MLLM}s",
author = "Zhao, Xiangyu and
Xu, Wanghan and
Liu, Bo and
Zhou, Yuhao and
Ling, Fenghua and
Fei, Ben and
Yue, Xiaoyu and
Bai, Lei and
Zhang, Wenlong and
Wu, Xiao-Ming",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.239/",
pages = "5270--5301",
ISBN = "979-8-89176-390-6",
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."
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%0 Conference Proceedings
%T MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs
%A Zhao, Xiangyu
%A Xu, Wanghan
%A Liu, Bo
%A Zhou, Yuhao
%A Ling, Fenghua
%A Fei, Ben
%A Yue, Xiaoyu
%A Bai, Lei
%A Zhang, Wenlong
%A Wu, Xiao-Ming
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhao-etal-2026-msearth
%X 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.
%U https://aclanthology.org/2026.acl-long.239/
%P 5270-5301
Markdown (Informal)
[MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs](https://aclanthology.org/2026.acl-long.239/) (Zhao et al., ACL 2026)
ACL
- Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, and Xiao-Ming Wu. 2026. MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5270–5301, San Diego, California, United States. Association for Computational Linguistics.