MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding
<|reference_start|>MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding: The rapid development of Multimodal Large Language Models (MLLMs) is making AI-driven scientific assistants increasingly feasible, with interpreting scientific figures being a crucial task. However, existing datasets and benchmarks focus mainly on basic charts and limited science subjects, lacking comprehensive evaluations. To address this, we curated a multimodal, multidisciplinary dataset from peer-reviewed, open-access Nature Communications articles, spanning 72 scientific disciplines. This dataset includes figures such as schematic diagrams, simulated images, macroscopic/microscopic photos, and experimental visualizations (e.g., western blots), which often require graduate-level, discipline-specific expertise to interpret. We developed benchmarks for scientific figure captioning and multiple-choice questions, evaluating six proprietary and over ten open-source models across varied settings. The results highlight the high difficulty of these tasks and the significant performance gap among models. While many open-source models performed at chance level on the multiple-choice task, some matched the performance of proprietary models. However, the gap was more pronounced in the captioning task. Our dataset also provide valuable resource for training. Fine-tuning the Qwen2-VL-2B model with our task-specific multimodal training data improved its multiple-choice accuracy to a level comparable to GPT-4o, though captioning remains challenging. Continuous pre-training of MLLMs using our interleaved article and figure data enhanced their material generation capabilities, demonstrating potential for integrating scientific knowledge. The dataset and benchmarks will be released to support further research.<|reference_end|>
@article{li2024mmsci:,
    title={MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal
  Scientific Understanding},
    author={Zekun Li, Xianjun Yang, Kyuri Choi, Wanrong Zhu, Ryan Hsieh, HyeonJung
  Kim, Jin Hyuk Lim, Sungyoung Ji, Byungju Lee, Xifeng Yan, Linda Ruth Petzold,
  Stephen D. Wilson, Woosang Lim, William Yang Wang},
    journal={arXiv preprint arXiv:2407.04903},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04903},
    primaryClass={cs.CL cs.AI cs.CV}
}