3.4 Natural Science and Engineering
Evaluating the performance of LLMs in natural science and engineering fields can help guide
applications and development in scientific research, technology development, and engineering
studies.
3.4.1 Mathematics. For fundamental mathematical problems, most large language models (LLMs)
demonstrate proficiency in addition and subtraction, and possess some capability in multiplication.
However, they face challenges when it comes to division, exponentiation, trigonometry functions,
and logarithm functions. On the other hand, LLMs exhibit competence in handling decimal numbers,
negative numbers, and irrational numbers [228]. In terms of performance, ChatGPT and GPT-4
outperform other models significantly, showcasing their superiority in solving mathematical tasks
[209]. These two models have a distinct advantage in dealing with large numbers (greater than
1e12) and complex, lengthy mathematical queries. GPT-4 outperforms ChatGPT by achieving a
ASurveyonEvaluationofLargeLanguageModels