Year: 2021
Type: preprint
Abstract: Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding role. We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic. To this end, we propose CUGE, a Chinese Language Understanding and Generation Evaluation benchmark with the following features: (1) Hierarchical benchmark framework, where datasets are principally selected and organized with a language capability-task-dataset hierarchy. (2) Multi-level scoring strategy, where different levels of model performance are provided based on the hierarchical framework. To facilitate CUGE, we provide a public leaderboard that can be customized to support flexible model judging criteria. Evaluation results on representative pre-trained language models indicate ample room for improvement towards general-purpose language intelligence. CUGE is publicly available at cuge.baai.ac.cn.
Source: arXiv (Cornell University)
Authors: Yao, Yuan, Dong, Qingxiu, Guan, Jian, Cao, Boxi, Zhang, Zhengyan, Xiao, Chaojun, Wang, Xiaozhi, Qi, Fanchao, Bao, Junwei, Nie, Jinran, Zeng, Zheni, Gu, Yuxian, Zhou, Kun, Huang, Xuancheng, Li, Wenhao, Ren, Shuhuai, Lu, Jinliang, Xu, Chengqiang, Wang, Huadong, Zeng, Guoyang, Zhou, Zile, Zhang, Jiajun, Li, Juanzi, Huang, Minlie, Yan, Rui, He, Xiaodong, Wan, Xiaojun, Zhao, Xin, Sun, Xu, Liu, Yang, Liu, Zhiyuan, Han, Xianpei, Yang, Erhong, Sui, Zhifang, Sun, Maosong
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4226476765