Chart data extraction is still tedious or unreliable.
We study MLLMs and find a key gap: they get the structure right but miss the numbers.
We introduce a benchmark + human-inspired training approach, and explore mixed-initiative workflows.
Come talk to us at #CHI2026!
Just presented our paper ReSpark at #UIST2025! ✨
ReSpark leverages previous data reports to help LLMs generate new analysis code, charts, and narratives—making data report authoring more efficient and logical.
Code is open source 👉 https://t.co/KqHF9hNzS9
@zjuidg@bebincaa
Amazing first #chi2025! Check our paper about video data programming, which helps users define labeling functions to cold-start TAL model training: “ProTAL: A Drag-and-Link Video Programming Framework for Temporal Action Localization.”
https://t.co/6NkespcQgB