SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation
<|reference_start|>SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation: Large multimodal models (LMMs) have proven flexible and generalisable across many tasks and fields. Although they have strong potential to aid scientific research, their capabilities in this domain are not well characterised. A key aspect of scientific research is the ability to understand and interpret figures, which serve as a rich, compressed source of complex information. In this work, we present SciFIBench, a scientific figure interpretation benchmark. Our main benchmark consists of a 1000-question gold set of multiple-choice questions split between two tasks across 12 categories. The questions are curated from CS arXiv paper figures and captions, using adversarial filtering to find hard negatives and human verification for quality control. We evaluate 26 LMMs on SciFIBench, finding it to be a challenging benchmark. Finally, we investigate the alignment and reasoning faithfulness of the LMMs on augmented question sets from our benchmark. We release SciFIBench to encourage progress in this domain.<|reference_end|>
@article{roberts2024scifibench:,
    title={SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure
  Interpretation},
    author={Jonathan Roberts, Kai Han, Neil Houlsby, Samuel Albanie},
    journal={arXiv preprint arXiv:2405.08807},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08807},
    primaryClass={cs.CV}
}