2023/04/30 by Dongyu Gong, Gong, Dongyu, Wan, Xingchen +1 · 4 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Biological sciences #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Neurons and Cognition (q-bio.NC) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2305.03731
openalex publication_date 2023/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.