Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness

Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella, Pritika Ramu, Vivek Gupta, Dan Roth


Abstract
Chart question answering (CQA) is a crucial area of Visual Language Understanding. However, the robustness and consistency of current Visual Language Models (VLMs) in this field remain under-explored. This paper evaluates state-of-the-art VLMs on comprehensive datasets, developed specifically for this study, encompassing diverse question categories and chart formats. We investigate two key aspects: 1) the models’ ability to handle varying levels of chart and question complexity, and 2) their robustness across different visual representations of the same underlying data. Our analysis reveals significant performance variations based on question and chart types, highlighting both strengths and weaknesses of current models. Additionally, we identify areas for improvement and propose future research directions to build more robust and reliable CQA systems. This study sheds light on the limitations of current models and paves the way for future advancements in the field.
Anthology ID:
2024.findings-emnlp.973
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
16696–16717
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.973
DOI:
10.18653/v1/2024.findings-emnlp.973
Bibkey:
Cite (ACL):
Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella, Pritika Ramu, Vivek Gupta, and Dan Roth. 2024. Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 16696–16717, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness (Mukhopadhyay et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-emnlp.973.pdf