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    <abstract>Recently, interpreting complex charts with logical reasoning has emerged as challenges due to the development of vision-language models. A prior state-of-the-art (SOTA) model has presented an end-to-end method that leverages the vision-language model to convert charts into table format utilizing Large Language Model (LLM) for reasoning. However, unlike natural images, charts contain a mix of essential and irrelevant information required for chart reasoning, and we discover that this characteristic can lower the performance of chart-to-table extraction. In this paper, we introduce SIMPLOT, a method designed to extract only the elements necessary for chart reasoning. The proposed method involves two steps: 1) training to mimic a simple plot that contains only the essential information from a complex chart for table extraction, followed by 2) performing reasoning based on the table. Our model enables accurate chart reasoning without the need for additional annotations or datasets, and its effectiveness is demonstrated through various experiments.</abstract>
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%0 Conference Proceedings
%T SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials
%A Kim, Wonjoong
%A Park, Sangwu
%A In, Yeonjun
%A Han, Seokwon
%A Park, Chanyoung
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F kim-etal-2025-simplot
%X Recently, interpreting complex charts with logical reasoning has emerged as challenges due to the development of vision-language models. A prior state-of-the-art (SOTA) model has presented an end-to-end method that leverages the vision-language model to convert charts into table format utilizing Large Language Model (LLM) for reasoning. However, unlike natural images, charts contain a mix of essential and irrelevant information required for chart reasoning, and we discover that this characteristic can lower the performance of chart-to-table extraction. In this paper, we introduce SIMPLOT, a method designed to extract only the elements necessary for chart reasoning. The proposed method involves two steps: 1) training to mimic a simple plot that contains only the essential information from a complex chart for table extraction, followed by 2) performing reasoning based on the table. Our model enables accurate chart reasoning without the need for additional annotations or datasets, and its effectiveness is demonstrated through various experiments.
%R 10.18653/v1/2025.findings-naacl.35
%U https://aclanthology.org/2025.findings-naacl.35/
%U https://doi.org/10.18653/v1/2025.findings-naacl.35
%P 573-593

Markdown (Informal)

[SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials](https://aclanthology.org/2025.findings-naacl.35/) (Kim et al., Findings 2025)

• SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials (Kim et al., Findings 2025)

ACL

• Wonjoong Kim, Sangwu Park, Yeonjun In, Seokwon Han, and Chanyoung Park. 2025. SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 573–593, Albuquerque, New Mexico. Association for Computational Linguistics.