Challenge: Existing research on chart understanding has been limited to single chart images.
Approach: They propose a dataset specifically designed for question answering over multi-chart images.
Outcome: The proposed method shows a 27.4% LLM-based accuracy drop on human-authored questions and a 5.39% gain in the human-generated questions.

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POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)

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Challenge: Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences.
Approach: They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse.
Outcome: The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages.
MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems (2025.naacl-long)

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Challenge: Existing chart understanding benchmarks focus on single-chart tasks, neglecting multi-hop reasoning required to extract and integrate information from multiple charts.
Approach: They propose a benchmark that evaluates MLLMs’ capabilities in four key areas: direct question answering, parallel question answering and comparative reasoning.
Outcome: The proposed benchmark evaluates MLLMs’ capabilities in four key areas: direct question answering, parallel question answering and comparative reasoning.
Chart Question Answering from Real-World Analytical Narratives (2025.acl-srw)

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Challenge: a dataset for chart question answering is constructed from visualization notebooks . data visualizations are an essential modality for communicating complex information about data.
Approach: They propose a dataset for chart question answering constructed from visualization notebooks . they use real-world, multi-view charts paired with natural language questions .
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ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)

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Challenge: Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models.
Approach: They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs.
Outcome: The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought.
ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering (2024.findings-emnlp)

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Challenge: Chart question answering (ChartQA) tasks are a critical part of visualization charts.
Approach: They propose a chart question answering task that uses MLLMs to analyze charts . they propose 'Chain-of-Charts' textual prompt strategy that directs attention to visual elements .
Outcome: The proposed model improves performance by 14.41% and 80% in low-level ChartQA tasks.
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning (2022.findings-acl)

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Challenge: Existing datasets that focus on complex reasoning questions do not address such questions as they are template-based and answers come from a fixed-vocabulary.
Approach: They propose a large-scale benchmark that uses visual and logical reasoning to answer questions using a transformer-based model.
Outcome: The proposed models achieve state-of-the-art on the previous datasets and on the current one, but also show that they have several challenges in answering complex reasoning questions.
WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts (2025.findings-acl)

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Challenge: Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU).
Approach: They propose a benchmark for evaluating cross-modal reasoning over tables and charts extracted from 4,000 Wikipedia pages . they evaluate 12 vision-language models that achieve 70% accuracy when provided with direct context .
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ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering (2025.findings-acl)

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Challenge: Chart Question Answering systems are limited in their ability to interpret data visually and reason with visual representations.
Approach: They propose a chart-based chart question-answering system that includes 1,341 charts from 99 diverse sources and 1,948 questions in various types.
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Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness (2024.findings-emnlp)

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Challenge: Chart question answering (CQA) is a crucial area of Visual Language Understanding.
Approach: They evaluate the robustness and consistency of current Visual Language Models on a dataset encompassing diverse question categories and chart formats.
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FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts (2024.findings-acl)

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Challenge: Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills.
Approach: They propose to use flowcharts as visual contexts to assess the capabilities of visual question-answering multimodal language models in reasoning.
Outcome: The proposed benchmarks evaluate models' ability to follow visual information without pre-existing knowledge on a suite of open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias.

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