MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.
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ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
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abstract
Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.
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representative citing papers
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MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.
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InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information
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VGR: Visual Grounded Reasoning
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From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
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FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding
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R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization
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ViQ: Text-Aligned Visual Quantized Representations at Any Resolution
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ViTexQA: A Multi-Frame Temporal Perception Dataset for Video Text Question Answering
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HPP: Hierarchical Programmatic Probing for Long Video Understanding by Decoupling Perception and Reasoning
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From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
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Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models
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Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing
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EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews
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LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning
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LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
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SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
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From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
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Evaluating Reasoning Fidelity in Visual Text Generation
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Semantic-Enriched Latent Visual Reasoning
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