  Large Language Models(LLMs) have demonstrated remarkable performance across
various natural language processing tasks; however, how to comprehensively and
accurately assess their performance becomes an urgent issue to be addressed.
This paper introduces GAOKAO-Bench, an intuitive benchmark that employs
questions from the Chinese GAOKAO examination as test samples, including both
subjective and objective questions. To align with human examination methods, we
design a method based on zero-shot settings to evaluate the performance of
LLMs. With human evaluation, we obtain the converted total score of LLMs,
including GPT-4, ChatGPT and ERNIE-Bot.Our findings reveal that LLMs have
achieved competitive scores in Chinese GAOKAO examination, while they exhibit
significant performance disparities across various subjects. We also use LLMs
to grade the subjective questions, and find that model scores achieve a
moderate level of consistency with human scores. In conclusion, this research
contributes a robust evaluation benchmark for future large language models and
offers valuable insights into the advantages and limitations of such models.
