Rethinking Comprehensive Benchmark for Chart Understanding: A Perspective from Scientific Literature

• URL: http://arxiv.org/abs/2412.12150v1
• Date: Wed, 11 Dec 2024 05:29:54 GMT
• Title: Rethinking Comprehensive Benchmark for Chart Understanding: A Perspective from Scientific Literature
• Authors: Lingdong Shen, Qigqi, Kun Ding, Gaofeng Meng, Shiming Xiang,
• Abstract summary: We introduce a new benchmark, Scientific Chart QA (SCI-CQA), which emphasizes flowcharts as a critical yet often overlooked category. We curated a dataset of 202,760 image-text pairs from 15 top-tier computer science conferences papers over the past decade. SCI-CQA also introduces a novel evaluation framework inspired by human exams, encompassing 5,629 carefully curated questions.
• Score: 33.69273440337546
• License:
• Abstract: Scientific Literature charts often contain complex visual elements, including multi-plot figures, flowcharts, structural diagrams and etc. Evaluating multimodal models using these authentic and intricate charts provides a more accurate assessment of their understanding abilities. However, existing benchmarks face limitations: a narrow range of chart types, overly simplistic template-based questions and visual elements, and inadequate evaluation methods. These shortcomings lead to inflated performance scores that fail to hold up when models encounter real-world scientific charts. To address these challenges, we introduce a new benchmark, Scientific Chart QA (SCI-CQA), which emphasizes flowcharts as a critical yet often overlooked category. To overcome the limitations of chart variety and simplistic visual elements, we curated a dataset of 202,760 image-text pairs from 15 top-tier computer science conferences papers over the past decade. After rigorous filtering, we refined this to 37,607 high-quality charts with contextual information. SCI-CQA also introduces a novel evaluation framework inspired by human exams, encompassing 5,629 carefully curated questions, both objective and open-ended. Additionally, we propose an efficient annotation pipeline that significantly reduces data annotation costs. Finally, we explore context-based chart understanding, highlighting the crucial role of contextual information in solving previously unanswerable questions.

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