Chart Question Answering from Real-World Analytical Narratives
Hutchinson, M. 
ORCID: 0009-0008-0983-7524, Jianu, R. ORCID: 0000-0002-5834-2658, Slingsby, A. ORCID: 0000-0003-3941-553X , Wood, J. ORCID: 0000-0001-9270-247X & Madhyastha, P. ORCID: 0000-0002-4438-8161 (2025).
Chart Question Answering from Real-World Analytical Narratives.
In:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop).
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), Jul 2025 - Jul 2025, Vienna, Austria.
doi: 10.18653/v1/2025.acl-srw.50
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.
| Publication Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
| Departments: | School of Science & Technology School of Science & Technology > Department of Computer Science School of Science & Technology > Department of Computer Science > giCentre |
| SWORD Depositor: |
Available under License Creative Commons Attribution.
Download (3MB) | Preview
Export
Downloads
Downloads per month over past year
Metadata
MetadataActions (login required)
Admin Login