Chart Question Answering from Real-World Analytical Narratives
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:26X3L5VOrecord.jsonopen to challenge →
classification
cs.CL
keywords
analyticalansweringchartdatasetlanguagenarrativesquestionreal-world
read the original abstract
We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural language questions grounded in analytical narratives. Unlike prior benchmarks, our data reflects ecologically valid reasoning workflows. Benchmarking state-of-the-art multimodal large language models reveals a significant performance gap, with GPT-4.1 achieving an accuracy of 69.3%, underscoring the challenges posed by this more authentic CQA setting.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.