ErrorRadar is a new benchmark of 2,500 multimodal K-12 math problems for MLLM error step identification and categorization, where GPT-4o trails human experts by ~10%.
Cmmu: A benchmark for chinese multi-modal multi-type question understanding and reasoning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
Introduces CFMME benchmark and reports that top LVLMs reach 66.11% accuracy on financial QA and 77.18 average on detection, recognition, and extraction tasks.
Current VLMs score well on Chinese-art recognition QA but collapse on style-to-period inference, expert-style long-form appreciation, and authenticity discrimination under visual confounds.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
citing papers explorer
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ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection
ErrorRadar is a new benchmark of 2,500 multimodal K-12 math problems for MLLM error step identification and categorization, where GPT-4o trails human experts by ~10%.
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Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset
Introduces CFMME benchmark and reports that top LVLMs reach 66.11% accuracy on financial QA and 77.18 average on detection, recognition, and extraction tasks.
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CArtBench: Evaluating Vision-Language Models on Chinese Art Understanding, Interpretation, and Authenticity
Current VLMs score well on Chinese-art recognition QA but collapse on style-to-period inference, expert-style long-form appreciation, and authenticity discrimination under visual confounds.
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Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.