CS-Bench: A Comprehensive Benchmark for Large Language Models towards
  Computer Science Mastery
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
https://github.com/csbench/csbench.
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Weiran Xu
CS-Bench: A Comprehensive Benchmark for Large Language Models towards
  Computer Science Mastery
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
https://github.com/csbench/csbench.
CS-Bench evaluates large language models across various subfields of computer science, uncovering performance trends and pinpointing areas for improvement.
large language models LLMs CS-Bench bilingual benchmark computer science test samples subfields knowledge supplementation CS-specific reasoning diverse reasoning capabilities