  Recent NLP tasks have benefited a lot from pre-trained language models (LM)
since they are able to encode knowledge of various aspects. However, current LM
evaluations focus on downstream performance, hence lack to comprehensively
inspect in which aspect and to what extent have they encoded knowledge. This
paper addresses both queries by proposing four tasks on syntactic, semantic,
commonsense, and factual knowledge, aggregating to a total of $39,308$
questions covering both linguistic and world knowledge in Chinese. Throughout
experiments, our probes and knowledge data prove to be a reliable benchmark for
evaluating pre-trained Chinese LMs. Our work is publicly available at
https://github.com/ZhiruoWang/ChnEval.
