  Working memory is a critical aspect of both human intelligence and artificial
intelligence, serving as a workspace for the temporary storage and manipulation
of information. In this paper, we systematically assess the working memory
capacity of ChatGPT, a large language model developed by OpenAI, by examining
its performance in verbal and spatial n-back tasks under various conditions.
Our experiments reveal that ChatGPT has a working memory capacity limit
strikingly similar to that of humans. Furthermore, we investigate the impact of
different instruction strategies on ChatGPT's performance and observe that the
fundamental patterns of a capacity limit persist. From our empirical findings,
we propose that n-back tasks may serve as tools for benchmarking the working
memory capacity of large language models and hold potential for informing
future efforts aimed at enhancing AI working memory.
