CLEVA: Chinese Language Models EVAluation Platform (2308.04813v2)
Abstract: With the continuous emergence of Chinese 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.
- Yanyang Li (22 papers)
- Jianqiao Zhao (6 papers)
- Duo Zheng (13 papers)
- Zi-Yuan Hu (6 papers)
- Zhi Chen (235 papers)
- Xiaohui Su (1 paper)
- Yongfeng Huang (110 papers)
- Shijia Huang (11 papers)
- Dahua Lin (336 papers)
- Michael R. Lyu (176 papers)
- Liwei Wang (239 papers)