  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.
