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As Large Language Models (LLMs) are becoming prevalent in various fields,there is an urgent need for improved NLP benchmarks that encompass all thenecessary knowledge of individual discipline. Many contemporary benchmarks forfoundational models emphasize a broad range of subjects but often fall short inpresenting all the critical subjects and encompassing necessary professionalknowledge of them. This shortfall has led to skewed results, given that LLMsexhibit varying performance across different subjects and knowledge areas. Toaddress this issue, we present psybench, the first comprehensive Chineseevaluation suite that covers all the necessary knowledge required for graduateentrance exams. psybench offers a deep evaluation of a model's strengths andweaknesses in psychology through multiple-choice questions. Our findings showsignificant differences in performance across different sections of a subject,highlighting the risk of skewed results when the knowledge in test sets is notbalanced. Notably, only the ChatGPT model reaches an average accuracy above$70\%$, indicating that there is still plenty of room for improvement. Weexpect that psybench will help to conduct thorough evaluations of base models'strengths and weaknesses and assist in practical application in the field ofpsychology.

PsyBench: a balanced and in-depth Psychological Chinese Evaluation Benchmark for Foundation Models