Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering

• URL: http://arxiv.org/abs/2503.18172v3
• Date: Tue, 15 Apr 2025 15:48:57 GMT
• Title: Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering
• Authors: Zixin Chen, Sicheng Song, Kashun Shum, Yanna Lin, Rui Sheng, Huamin Qu,
• Abstract summary: Misleading chart visualizations can distort perceptions and lead to incorrect conclusions.<n>Recent advances in large language models (MLLMs) have demonstrated strong chart comprehension capabilities.<n>This paper introduces the Misleading Chart Question Answering (Misleading ChartQA) Benchmark, a large-scale dataset designed to assess MLLMs in identifying and reasoning about misleading charts.
• Score: 28.54154468156412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Misleading chart visualizations, which intentionally manipulate data representations to support specific claims, can distort perceptions and lead to incorrect conclusions. Despite decades of research, misleading visualizations remain a widespread and pressing issue. Recent advances in multimodal large language models (MLLMs) have demonstrated strong chart comprehension capabilities, yet no existing work has systematically evaluated their ability to detect and interpret misleading charts. This paper introduces the Misleading Chart Question Answering (Misleading ChartQA) Benchmark, a large-scale multimodal dataset designed to assess MLLMs in identifying and reasoning about misleading charts. It contains over 3,000 curated examples, covering 21 types of misleaders and 10 chart types. Each example includes standardized chart code, CSV data, and multiple-choice questions with labeled explanations, validated through multi-round MLLM checks and exhausted expert human review. We benchmark 16 state-of-the-art MLLMs on our dataset, revealing their limitations in identifying visually deceptive practices. We also propose a novel pipeline that detects and localizes misleaders, enhancing MLLMs' accuracy in misleading chart interpretation. Our work establishes a foundation for advancing MLLM-driven misleading chart comprehension. We publicly release the sample dataset to support further research in this critical area.

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