@inproceedings{li-etal-2023-cleva,
title = "{CLEVA}: {C}hinese Language Models {EVA}luation Platform",
author = "Li, Yanyang and
Zhao, Jianqiao and
Zheng, Duo and
Hu, Zi-Yuan and
Chen, Zhi and
Su, Xiaohui and
Huang, Yongfeng and
Huang, Shijia and
Lin, Dahua and
Lyu, Michael and
Wang, Liwei",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.17",
doi = "10.18653/v1/2023.emnlp-demo.17",
pages = "186--217",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T CLEVA: Chinese Language Models EVAluation Platform
%A Li, Yanyang
%A Zhao, Jianqiao
%A Zheng, Duo
%A Hu, Zi-Yuan
%A Chen, Zhi
%A Su, Xiaohui
%A Huang, Yongfeng
%A Huang, Shijia
%A Lin, Dahua
%A Lyu, Michael
%A Wang, Liwei
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-cleva
%X 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.
%R 10.18653/v1/2023.emnlp-demo.17
%U https://aclanthology.org/2023.emnlp-demo.17
%U https://doi.org/10.18653/v1/2023.emnlp-demo.17
%P 186-217
Markdown (Informal)
[CLEVA: Chinese Language Models EVAluation Platform](https://aclanthology.org/2023.emnlp-demo.17) (Li et al., EMNLP 2023)
ACL
- Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, and Liwei Wang. 2023. CLEVA: Chinese Language Models EVAluation Platform. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 186–217, Singapore. Association for Computational Linguistics.