CMMLU is an evaluation benchmark designed for the Chinese context, covering 67 topics to comprehensively test language models' knowledge and reasoning abilities, with a p...
CMMLUIn today’s rapidly advancing AI era, evaluating the capabilities of language models has become crucial. CMMLU (Chinese Massive Multitask Language Understanding) is designed for this purpose; it is a comprehensive evaluation benchmark tailored for the Chinese context, aiming to thoroughly test language models’ knowledge and reasoning abilities.
CMMLU consists of a set of multiple-choice questions across multiple disciplines, covering 67 topics ranging from basic subjects to advanced professional levels, including natural sciences, humanities, social sciences, and China-specific common knowledge.
The uniqueness of CMMLU lies in its high localization; many tasks have China-specific answers that may not be applicable in other regions or languages. This makes CMMLU a truly localized Chinese test benchmark, enabling more accurate evaluation of language models’ performance in the Chinese context.
CMMLU shares similarities with other evaluation benchmarks like MMLU and C-Eval but focuses on the Chinese context, providing assessment standards more aligned with the needs of Chinese users.
Using CMMLU for evaluation can help developers identify deficiencies in models’ Chinese understanding and reasoning abilities, allowing targeted improvements to enhance performance in practical applications.
As an open-source project, CMMLU is freely available for researchers and developers, with resources accessible on GitHub.
The CMMLU project was initiated in 2023 by a group of researchers dedicated to enhancing Chinese language model evaluation standards, aiming to provide a comprehensive and professional assessment tool. Through CMMLU, users can gain in-depth insights into models’ performance in the Chinese context, identify potential issues, and implement targeted optimizations.
