GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models
<|reference_start|>GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models: Large multimodal models (LMMs) have exhibited proficiencies across many visual tasks. Although numerous well-known benchmarks exist to evaluate model performance, they increasingly have insufficient headroom. As such, there is a pressing need for a new generation of benchmarks challenging enough for the next generation of LMMs. One area that LMMs show potential is graph analysis, specifically, the tasks an analyst might typically perform when interpreting figures such as estimating the mean, intercepts or correlations of functions and data series. In this work, we introduce GRAB, a graph analysis benchmark, fit for current and future frontier LMMs. Our benchmark is entirely synthetic, ensuring high-quality, noise-free questions. GRAB is comprised of 2170 questions, covering four tasks and 23 graph properties. We evaluate 20 LMMs on GRAB, finding it to be a challenging benchmark, with the highest performing model attaining a score of just 21.7%. Finally, we conduct various ablations to investigate where the models succeed and struggle. We release GRAB to encourage progress in this important, growing domain.<|reference_end|>
@article{roberts2024grab:,
    title={GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models},
    author={Jonathan Roberts, Kai Han, Samuel Albanie},
    journal={arXiv preprint arXiv:2408.11817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11817},
    primaryClass={cs.CV}
}