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 influential Chinese LLMs have validated CLEVA's
efficacy.

为解决当前评估中文大型语言模型的挑战，我们提出了 CLEVA，一个用户友好的平台，通过标准化的流程评估模型的性能，并定期更新排行榜。CLEVA 还通过策略性的数据采样来减少数据污染，并通过易于使用的界面和模型 API 使用户能够进行全面评估。大规模实验已验证 CLEVA 的有效性。