Summary of C-eval: a Multi-level Multi-discipline Chinese Evaluation Suite For Foundation Models, by Yuzhen Huang et al.
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
by Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, Junxian He
First submitted to arxiv on: 15 May 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces C-Eval, a novel benchmark suite designed to evaluate the advanced knowledge and reasoning abilities of large language models (LLMs) in a Chinese context. The suite consists of multiple-choice questions across four difficulty levels, covering 52 diverse disciplines from humanities to science and engineering. C-Eval is accompanied by C-Eval Hard, a subset of challenging subjects that require advanced reasoning abilities. The authors evaluate the most advanced LLMs on C-Eval, including English- and Chinese-oriented models, finding that only GPT-4 achieves an average accuracy above 60%. This suggests room for improvement in current LLMs. The authors anticipate that C-Eval will help analyze strengths and shortcomings of foundation models, promoting their development and growth for Chinese users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary C-Eval is a new way to test how good big language models are at understanding and reasoning about complex information in Chinese. It’s like a big test with multiple-choice questions that cover lots of different subjects, from art to science. The questions get harder as you go along, so it’s not just for beginners! The people who made C-Eval tested some of the best language models out there and found that only one model, called GPT-4, did really well on the test. This means that there is still a lot of room for improvement in these language models, but with tools like C-Eval, they can get better and better. |
Keywords
» Artificial intelligence » Gpt
