With the continuous emergence of Chinese Large Language Models (LLMs), how to evaluate a model's capabilities has become an increasingly significant issue. The absence of a comprehensive Chinese benchmark that thoroughly assesses a model's performance, the unstandardized and incomparable prompting procedure, and the prevalent risk of contamination pose major challenges in the current evaluation of Chinese LLMs. We present CLEVA, a user-friendly platform crafted to holistically evaluate Chinese LLMs. Our platform employs a standardized workflow to assess LLMs' performance across various dimensions, regularly updating a competitive leaderboard. To alleviate contamination, CLEVA curates a significant proportion of new data and develops a sampling strategy that guarantees a unique subset for each leaderboard round. Empowered by an easy-to-use interface that requires just a few mouse clicks and a model API, users can conduct a thorough evaluation with minimal coding. Large-scale experiments featuring 23 Chinese LLMs have validated CLEVA's efficacy.
@article{arxiv.2308.04813,
title = {CLEVA: Chinese Language Models EVAluation Platform},
author = {Yanyang Li and Jianqiao Zhao and Duo Zheng and Zi-Yuan Hu and Zhi Chen and Xiaohui Su and Yongfeng Huang and Shijia Huang and Dahua Lin and Michael R. Lyu and Liwei Wang},
journal= {arXiv preprint arXiv:2308.04813},
year = {2023}
}