Artificial Intelligence

    

Spin Glass Theory and the Statistical Mechanics of Language Models

Authors: Eliza Kosloff

The recent success of large language models (LLMs) in artificial intelligence has drawn significant attention from the machine learning community. However, the theoretical foundations of these models remain poorly understood. In this paper, we explore the deep connections between LLMs and spin glass theory, a well-established framework in statistical physics. We show how key concepts from spin glasses, such as frustration, random interactions, and phase transitions, can provide a powerful lens for understanding the behavior of LLMs. We argue that this interdisciplinary perspective can facilitate knowledge transfer between the machine learning and physics communities, leading to novel insights and algorithmic improvements.

Comments: 3 Pages.

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Submission history

[v1] 2024-03-22 20:46:45

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