Scaling Laws for Associative Memories

Published: 16 Jan 2024, Last Modified: 15 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: scaling law, associative memory, mechanistic interpretability, Hopfield network
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TL;DR: Mechanistic interpretability of memorization (modeling transformer intermediate layers)
Abstract: Learning arguably involves the discovery and memorization of abstract rules. The aim of this paper is to study associative memory mechanisms. Our model is based on high-dimensional matrices consisting of outer products of embeddings, which relates to the inner layers of transformer language models. We derive precise scaling laws with respect to sample size and parameter size, and discuss the statistical efficiency of different estimators, including optimization-based algorithms. We provide extensive numerical experiments to validate and interpret theoretical results, including fine-grained visualizations of the stored memory associations.
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Primary Area: learning theory
Submission Number: 5072