CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning
This provides a domain-specific evaluation tool for researchers and developers working on Chinese MLLMs, though it is incremental as it extends existing benchmark concepts to a new language and format.
The authors tackled the lack of comprehensive benchmarks for evaluating multi-modal large language models (MLLMs) in Chinese by introducing CMMU, a benchmark with 3,603 multi-type questions across 7 subjects, which posed significant challenges to current MLLMs, including GPT4-V and Gemini-Pro.
Multi-modal large language models(MLLMs) have achieved remarkable progress and demonstrated powerful knowledge comprehension and reasoning abilities. However, the mastery of domain-specific knowledge, which is essential for evaluating the intelligence of MLLMs, continues to be a challenge. Current multi-modal benchmarks for domain-specific knowledge concentrate on multiple-choice questions and are predominantly available in English, which imposes limitations on the comprehensiveness of the evaluation. To this end, we introduce CMMU, a novel benchmark for multi-modal and multi-type question understanding and reasoning in Chinese. CMMU consists of 3,603 questions in 7 subjects, covering knowledge from primary to high school. The questions can be categorized into 3 types: multiple-choice, multiple-response, and fill-in-the-blank, bringing greater challenges to MLLMs. In addition, we propose an evaluation strategy called Positional Error Variance for assessing multiple-choice questions. The strategy aims to perform a quantitative analysis of position bias. We evaluate seven open-source MLLMs along with GPT4-V, Gemini-Pro, and Qwen-VL-Plus. The results demonstrate that CMMU poses a significant challenge to the recent MLLMs. The data and code are available at https://github.com/FlagOpen/CMMU.