Janik (2023)

Aspects of human memory and Large Language Models

arXiv

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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.

Reference

Janik, R. A. (2023). Aspects of human memory and Large Language Models. arXiv preprint arXiv:2311.03839. https://doi.org/10.48550/arxiv.2311.03839

What Rests on This

1 mechanism cites this source. Each finding below is quoted as it appears on that mechanism's own page.

Forgetting Memory Processes

Measured in AI

A 2023 preprint probing GPT-J found only a slow decline in recall as neutral filler text was added between a fact and the question, a clear benefit when a repetition was spaced rather than massed, and much larger losses from interfering facts; comparisons with human data were qualitative