A Law of Next-Token Prediction in Large Language Models

Hangfeng He; Weijie J. Su

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arXiv · 2408.13442

A Law of Next-Token Prediction in Large Language Models

Hangfeng He·Weijie J. Su

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Abstract

Large language models (LLMs) have been widely employed across various application domains, yet their black-box nature poses significant challenges to understanding how these models process input data internally to make predictions. In this paper, we introduce a precise and quantitative law that governs the learning of contextualized token embeddings through intermediate layers in pre-trained LLMs for next-token prediction. Our findings reveal that each layer contributes equally to enhancing prediction accuracy, from the lowest to the highest layer -- a universal phenomenon observed across a diverse array of open-source LLMs, irrespective of their architectures or pre-training data. We demonstrate that this law offers new perspectives and actionable insights to inform and guide practices in LLM development and applications, including model scaling, pre-training tasks, and interpretation.

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cs.LG cs.AI cs.CL stat.ML

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Hangfeng He, Weijie J. Su. 2024-08-24. A Law of Next-Token Prediction in Large Language Models. https://arxiv.org/abs/2408.13442

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