Closing the Gap: CMMMU Benchmarking Bilingual LMMs for Expert-Level AI

Introducing the CMMMU Benchmark for AI Evaluation

In the realm of artificial intelligence, Large Multimodal Models (LMMs) have shown impressive problem-solving capabilities in various areas such as zero-shot image/video classification, zero-shot image/video-text retrieval, and multimodal question answering (QA). However, there’s a significant gap between powerful LMMs and expert-level artificial intelligence, especially in tasks involving complex perception and reasoning with domain-specific knowledge.

Enter CMMMU, a groundbreaking Chinese benchmark meticulously designed to evaluate LMMs’ performance on a wide range of multidisciplinary tasks, guiding the development of bilingual LMMs towards achieving expert-level artificial intelligence.

What is CMMMU?

CMMMU (Chinese Massive Multi-discipline Multimodal Understanding) stands out as one of the most comprehensive benchmarks, comprising 12,000 manually collected Chinese multimodal questions spanning six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.

There is a rigorous three-stage data collection process that ensures the richness and diversity of CMMMU. At least one of the paper’s authors manually verifies each question, and there’s a mechanism to filter out unqualified questions with images, ensuring a balanced dataset across disciplines.

Evaluating LMMs’ Performance

The CMMMU evaluation includes large language models (LLMs) and large multimodal models (LMMs) in both closed-source and open-source implementations using zero-shot evaluation settings. A robust evaluation pipeline ensures a comprehensive assessment of the model’s ability to generate accurate answers on multimodal tasks, using micro-average accuracy as the evaluation metric.

What the Analysis Revealed

The paper also presents a thorough error analysis of 300 samples, showcasing instances where even top-performing LMMs answer incorrectly. Surprisingly, the study reveals a smaller performance gap between open-source and closed-source LMMs in a Chinese context compared to English, with the potential of some open-source LMMs in the Chinese language domain.

Conclusion

CMMMU represents a significant advancement in the quest for Advanced General Intelligence (AGI). It serves as a meticulous evaluator of the latest Large Multimodal Models (LMMs), gauging their elementary perceptual skills, intricate logical reasoning, and profound domain-specific expertise.

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.

The CMMMU benchmark is a key development for AI researchers and developers, offering a thorough evaluation of LMMs’ capabilities. Check out the paper and project to learn more about the CMMMU benchmark and its potential impact on AI development.

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