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    <abstract>Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components. Our observations reveal that the perception capabilities of existing large vision-language models (LVLMs) constitute a critical bottleneck in this process. In this study, we delve into this perception bottleneck by decomposing it into two components: the vision encoder bottleneck, where the visual representation may fail to encapsulate the correct information, and the extraction bottleneck, where the language model struggles to extract the necessary information from the provided visual representations. Through comprehensive experiments, we find that (1) the information embedded within visual representations is substantially richer than what is typically captured by linear extractors, such as the widely used retrieval accuracy metric; (2) While instruction tuning effectively enhances the extraction capability of LVLMs, the vision encoder remains a critical bottleneck, demanding focused attention and improvement. Therefore, we further enhance the visual encoder to mitigate the vision encoder bottleneck under a contrastive learning framework. Empirical results demonstrate that our approach significantly mitigates the perception bottleneck and improves the ability of LVLMs to comprehend charts.</abstract>
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%0 Conference Proceedings
%T On the Perception Bottleneck of VLMs for Chart Understanding
%A Liu, Junteng
%A Zeng, Weihao
%A Zhang, Xiwen
%A Wang, Yijun
%A Shan, Zifei
%A He, Junxian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-perception
%X Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components. Our observations reveal that the perception capabilities of existing large vision-language models (LVLMs) constitute a critical bottleneck in this process. In this study, we delve into this perception bottleneck by decomposing it into two components: the vision encoder bottleneck, where the visual representation may fail to encapsulate the correct information, and the extraction bottleneck, where the language model struggles to extract the necessary information from the provided visual representations. Through comprehensive experiments, we find that (1) the information embedded within visual representations is substantially richer than what is typically captured by linear extractors, such as the widely used retrieval accuracy metric; (2) While instruction tuning effectively enhances the extraction capability of LVLMs, the vision encoder remains a critical bottleneck, demanding focused attention and improvement. Therefore, we further enhance the visual encoder to mitigate the vision encoder bottleneck under a contrastive learning framework. Empirical results demonstrate that our approach significantly mitigates the perception bottleneck and improves the ability of LVLMs to comprehend charts.
%U https://aclanthology.org/2025.findings-emnlp.573/
%P 10829-10841

Markdown (Informal)

[On the Perception Bottleneck of VLMs for Chart Understanding](https://aclanthology.org/2025.findings-emnlp.573/) (Liu et al., Findings 2025)

• On the Perception Bottleneck of VLMs for Chart Understanding (Liu et al., Findings 2025)

ACL

• Junteng Liu, Weihao Zeng, Xiwen Zhang, Yijun Wang, Zifei Shan, and Junxian He. 2025. On the Perception Bottleneck of VLMs for Chart Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 10829–10841, Suzhou, China. Association for Computational Linguistics.