Tag
This paper introduces CoCoEvolve, a self-supervised method that improves cross-representation understanding across charts, tables, and code by enforcing one-to-one consistency between representations, with training-time and test-time co-evolution objectives.
CURV proposes a curriculum learning framework that enhances chart question answering by developing intrinsic visual grounded reasoning in multimodal LLMs, achieving significant improvements on real-world benchmarks.
A 4B open-source model beats Mythos 5 on the CharXiv chart understanding benchmark, showing strong performance from a freely available small model.
MIT researchers developed ChartNet, a dataset of over a million charts, to train vision-language models to interpret charts more accurately. Their open-source models outperform much larger commercial models on chart understanding tasks.
HakushoBench is a Japanese chart and table VQA benchmark built from governmental white papers to evaluate vision-language models' understanding of complex visual data, challenging open-weight models with a 58.6% accuracy and a 34.9-point gap to proprietary models.