Computer Science (CS) stands as a testament to the intricacies of human
intelligence, profoundly advancing the development of artificial intelligence
and modern society. However, the current community of large language models
(LLMs) overly focuses on benchmarks for analyzing specific foundational skills
(e.g. mathematics and code generation), neglecting an all-round evaluation of
the computer science field. To bridge this gap, we introduce CS-Bench, the
first bilingual (Chinese-English) benchmark dedicated to evaluating the
performance of LLMs in computer science. CS-Bench comprises approximately 5K
meticulously curated test samples, covering 26 subfields across 4 key areas of
computer science, encompassing various task forms and divisions of knowledge
and reasoning. Utilizing CS-Bench, we conduct a comprehensive evaluation of
over 30 mainstream LLMs, revealing the relationship between CS performance and
model scales. We also quantitatively analyze the reasons for failures in
existing LLMs and highlight directions for improvements, including knowledge
supplementation and CS-specific reasoning. Further cross-capability experiments
show a high correlation between LLMs' capabilities in computer science and
their abilities in mathematics and coding. Moreover, expert LLMs specialized in
mathematics and coding also demonstrate strong performances in several CS
subfields. Looking ahead, we envision CS-Bench serving as a cornerstone for LLM
applications in the CS field and paving new avenues in assessing LLMs' diverse
reasoning capabilities. The CS-Bench data and evaluation code are available at
this https URL

计算机科学（CS）是人类智能复杂性的证明，深刻推动了人工智能和现代社会的发展。我们引入 CS-Bench，这是第一个专门用于评估 LLM 在计算机科学领域性能的双语（中英文）基准，包括大约 5K 个精心策划的测试样本，涵盖计算机科学的 4 个关键领域的 26 个子领域，包括各种任务形式和知识推理的划分。利用 CS-Bench，我们对 30 多个主流 LLM 进行了全面评估，揭示了 CS 性能与模型规模之间的关系，并定量分析了现有 LLM 失败的原因，并强调了知识补充和 CS 特定推理等改进方向。进一步的跨能力实验显示 LLM 在计算机科学领域能力与数学和编码能力之间存在高度相关性。此外，专注于数学和编码的专家 LLM 在几个 CS 子领域也表现出强大的性能。展望未来，我们预见 CS-Bench 将成为 LLM 在 CS 领域应用的基石，并为评估 LLM 的多样化推理能力开辟新的途径。CS-Bench 的数据和评估代码可在此 https URL 上找到。