Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high school subjects and are confied 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 48.96%. 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.

SciBench is a carefully curated dataset of college-level scientific problems, collected from widely-used textbooks in college-level Chemistry, Physics, and Mathematics courses. Distinct from existing benchmarks, all of the problems are open-ended, free-response questions that demand multi-step reasoning abilities, the understanding of scientific concepts, the retrieval of domain-specific knowledge (e.g., equations and theorems), and complex numeric computation capabilities (e.g., calculus or differential equations).

To evaluate the capabilities and analyze the limitations of Large Language Models (LLMs) to solve scientific computing problems, we collect a new dataset consisting of college-level textbooks and course exams in a variety of domains. This section details the dataset construction process.

Data selection criteria.Our dataset aims to improve the previous benchmarks by including more challenging problems. Specifically, the selected dataset should fulfill the following requirements:

• Inclusion of college-level problems.The chosen problems demand a solid understanding of domain-specific knowledge, adept calculation skills, and the ability to perform complex numerical computations.
• Inclusion of detailed solutions.To facilitate a thorough analysis of the limitations of LLMs, detailed solutions should be provided as well, which could facilitate a finergrained examination of the capacity of LLMs to handle complex problem-solving tasks.
• Inclusion of visual elements.In real-world, many scientific problems require the interpretation and integration of both textual and visual information. The included problems should thus contain visual elements (such as figures) in the contexts.
• Inaccessibility in text formats.To ensure an unbiased evaluation, questions should not be readily accessible online and cannot be easily extracted or transformed into text. This aims to mitigate any potential information leakage from the exposure of LLMs to pre-existing online question banks, such as those found in standardized tests like the SAT exams.
• Assessment of advanced problem-solving capabilities.The problems to benchmark should not be confined to basic arithmetic operations like addition and multiplication. Rather, they should enable evaluating the capability of LLMs in performing advanced computations such as calculus and differential equations.