[2308.04813] CLEVA: Chinese Language Models EVAluation Platform
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Computer Science > Computation and Language

arXiv:2308.04813 (cs)
[Submitted on 9 Aug 2023 (v1), last revised 16 Oct 2023 (this version, v2)]

Title:CLEVA: Chinese Language Models EVAluation Platform

Authors:Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael R. Lyu, Liwei Wang
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Abstract: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.
Comments: EMNLP 2023 System Demonstrations camera-ready
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2308.04813 [cs.CL]
  (or arXiv:2308.04813v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2308.04813

Submission history

From: Jianqiao Zhao [view email]
[v1] Wed, 9 Aug 2023 09:11:31 UTC (1,790 KB)
[v2] Mon, 16 Oct 2023 11:32:14 UTC (1,802 KB)
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