Summary of Cs-bench: a Comprehensive Benchmark For Large Language Models Towards Computer Science Mastery, by Xiaoshuai Song et al.
CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
by Xiaoshuai Song, Muxi Diao, Guanting Dong, Zhengyang Wang, Yujia Fu, Runqi Qiao, Zhexu Wang, Dayuan Fu, Huangxuan Wu, Bin Liang, Weihao Zeng, Yejie Wang, Zhuoma GongQue, Jianing Yu, Qiuna Tan, Weiran Xu
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper introduces CS-Bench, a multilingual benchmark that evaluates the performance of large language models (LLMs) in computer science. The benchmark comprises approximately 10K test samples covering 26 subfields across four key areas of computer science. By using CS-Bench, the authors conduct a comprehensive evaluation of over 30 mainstream LLMs and analyze the relationship between their performance in computer science and model scales. They also highlight directions for improvements, including knowledge supplementation and CS-specific reasoning. The results show a high correlation between LLMs’ capabilities in computer science and their abilities in mathematics and coding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a benchmark called CS-Bench that helps large language models (LLMs) get better at computer science tasks. This is important because right now, people are only testing these models for simple math and code-writing skills, but not for real-world computer science work. The authors made 10K test questions in four languages to cover many different areas of computer science. They used this benchmark to test over 30 LLMs and found that bigger models do better on computer science tasks. They also found that models that are good at math and coding are usually good at some computer science tasks too. |
