Aspects of human memory and Large Language Models

2023/11/07 by Romuald A. Janik, Janik, Romuald A. · 2 citations
Computer Science · Materials Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Natural Language Processing Techniques #Neurons and Cognition (q-bio.NC) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2311.03839

openalex publication_date 2023/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Large Language Models (LLMs) are huge artificial neural networks which primarily serve to generate text, but also provide a very sophisticated probabilistic model of language use. Since generating a semantically consistent text requires a form of effective memory, we investigate the memory properties of LLMs and find surprising similarities with key characteristics of human memory. We argue that the human-like memory properties of the Large Language Model do not follow automatically from the LLM architecture but are rather learned from the statistics of the training textual data. These results strongly suggest that the biological features of human memory leave an imprint on the way that we structure our textual narratives.

Cited by

• Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMs
• Mixing Mechanisms: How Language Models Retrieve Bound Entities In-Context

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