  Most of the existing Large Language Model (LLM) benchmarks on scientific
problem reasoning focus on problems grounded in high-school subjects and are
confined to elementary algebraic operations. To systematically examine the
reasoning capabilities required for solving complex scientific problems, we
introduce an expansive benchmark suite SciBench for LLMs. SciBench contains a
carefully curated dataset featuring a range of collegiate-level scientific
problems from mathematics, chemistry, and physics domains. Based on the
dataset, we conduct an in-depth benchmarking study of representative
open-source and proprietary LLMs with various prompting strategies. The results
reveal that the current LLMs fall short of delivering satisfactory performance,
with the best overall score of merely 43.22%. Furthermore, through a detailed
user study, we categorize the errors made by LLMs into ten problem-solving
abilities. Our analysis indicates that no single prompting strategy
significantly outperforms the others and some strategies that demonstrate
improvements in certain problem-solving skills could result in declines in
other skills. We envision that SciBench will catalyze further developments in
the reasoning abilities of LLMs, thereby ultimately contributing to scientific
research and discovery.
