Exploring the intricacies of human memory and its analogous representation in ChatGPT

Habib Hamam


Human memory and ChatGPT both rely on associations and patterns to generate contextually relevant responses. We explore how they work in tandem. Both use associations to activate related information when prompted. Memory forms generic representations that become precise with added details, similar to ChatGPT's responses with specific prompts. Activation Through Cues: Memory and ChatGPT recall based on cues or prompts, influenced by input. Level of Detail: Memory constructs mental images based on information, just as ChatGPT responds to input details. Dynamic Nature: Both adapt to memorize repeated segments with diverse continuations. By understanding the dynamics of memory and its parallels with ChatGPT's response generation, researchers can further enhance the model's capabilities. Fine-tuning the model's ability to activate relevant information, generate specific responses, and adapt to varying levels of detail and specificity in the input can contribute to its overall performance and relevance in various language tasks.


ChatGPT; Differentiable neural computer; Human memory; Language models; Neural turing machine; Transformer model

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DOI: http://doi.org/10.11591/ijeecs.v34.i3.pp1760-1769


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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