The CMMMU benchmark has been introduced to bridge the gap between powerful Large Multimodal Models (LMMs) and expert-level artificial intelligence in tasks involving complex perception and reasoning with domain-specific knowledge. It comprises 12,000 Chinese multimodal questions across six core disciplines and employs a rigorous data collection and quality control process. The benchmark evaluates LMMs, presents an error analysis, and compares the performance of open-source and closed-source LMMs in Chinese and English contexts. Reference: https://arxiv.org/pdf/2401.11944.pdf
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Introducing CMMMU: A New Benchmark for Large Multimodal Models (LMMs)
In the realm of artificial intelligence, Large Multimodal Models (LMMs) have shown remarkable problem-solving capabilities across diverse tasks. However, there is a substantial gap between powerful LMMs and expert-level artificial intelligence, especially in tasks involving complex perception and reasoning with domain-specific knowledge.
What is CMMMU?
CMMMU (Chinese Massive Multi-discipline Multimodal Understanding) is a comprehensive benchmark comprising 12,000 manually collected Chinese multimodal questions sourced from college exams, quizzes, and textbooks. It evaluates LMMs on complex reasoning and perception tasks across six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.
Data Collection and Quality Control
A three-stage data collection process ensures the richness and diversity of CMMMU. The benchmark also implements a rigorous data quality control protocol to enhance data quality further.
Evaluation and Error Analysis
The evaluation includes large language models (LLMs) and large multimodal models (LMMs) using zero-shot evaluation settings. The paper also presents a thorough error analysis of 300 samples, showcasing instances where even top-performing LMMs answer incorrectly.
Key Findings
The study reveals a smaller performance gap between open-source and closed-source LMMs in a Chinese context compared to English. It also emphasizes the potential of certain open-source LMMs in the Chinese language domain.
Implications and Conclusion
The CMMMU benchmark represents a significant advancement in the quest for Advanced General Intelligence (AGI). It provides insights into the reasoning capacity of bilingual LMMs in Chinese and English contexts, paving the way for AGI that rivals seasoned professionals across diverse fields.
Practical AI Solutions for Middle Managers
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