Working Memory Capacity of ChatGPT: An Empirical Study
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866911769343033344 |
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| author | Gong, Dongyu Wan, Xingchen Wang, Dingmin |
| author_facet | Gong, Dongyu Wan, Xingchen Wang, Dingmin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03731 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Working Memory Capacity of ChatGPT: An Empirical Study Gong, Dongyu Wan, Xingchen Wang, Dingmin Artificial Intelligence Computation and Language Neurons and Cognition 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. |
| title | Working Memory Capacity of ChatGPT: An Empirical Study |
| topic | Artificial Intelligence Computation and Language Neurons and Cognition |
| url | https://arxiv.org/abs/2305.03731 |