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    <abstract>Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. To facilitate chart-based reasoning using natural language, various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language instructions. Despite the recent success of Large Language Models (LLMs) across diverse NLP tasks, their abilities and limitations in the realm of data visualization remain under-explored, possibly due to their lack of multi-modal capabilities. To bridge the gap, this paper presents one of the first comprehensive evaluations of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks. Our evaluation includes a comprehensive assessment of both closed and open-sourced LVLMs across five major chart reasoning tasks. Furthermore, we perform a qualitative evaluation of LVLMs’ performance on a diverse range of charts, aiming to provide a thorough analysis. Our findings reveal that while LVLMs demonstrate impressive abilities in generating fluent texts covering high-level data insights, they also encounter common problems like hallucinations, factual errors, and data bias. We highlight the key strengths and limitations of LVLMs in chart comprehension tasks, offering insights for future research</abstract>
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%0 Conference Proceedings
%T Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning
%A Islam, Mohammed Saidul
%A Rahman, Raian
%A Masry, Ahmed
%A Laskar, Md Tahmid Rahman
%A Nayeem, Mir Tafseer
%A Hoque, Enamul
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F islam-etal-2024-large
%X Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. To facilitate chart-based reasoning using natural language, various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language instructions. Despite the recent success of Large Language Models (LLMs) across diverse NLP tasks, their abilities and limitations in the realm of data visualization remain under-explored, possibly due to their lack of multi-modal capabilities. To bridge the gap, this paper presents one of the first comprehensive evaluations of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks. Our evaluation includes a comprehensive assessment of both closed and open-sourced LVLMs across five major chart reasoning tasks. Furthermore, we perform a qualitative evaluation of LVLMs’ performance on a diverse range of charts, aiming to provide a thorough analysis. Our findings reveal that while LVLMs demonstrate impressive abilities in generating fluent texts covering high-level data insights, they also encounter common problems like hallucinations, factual errors, and data bias. We highlight the key strengths and limitations of LVLMs in chart comprehension tasks, offering insights for future research
%U https://aclanthology.org/2024.findings-emnlp.191
%P 3334-3368

Markdown (Informal)

[Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning](https://aclanthology.org/2024.findings-emnlp.191) (Islam et al., Findings 2024)

• Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning (Islam et al., Findings 2024)

ACL

• Mohammed Saidul Islam, Raian Rahman, Ahmed Masry, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, and Enamul Hoque. 2024. Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3334–3368, Miami, Florida, USA. Association for Computational Linguistics.