A Law of Next-Token Prediction in Large Language Models

• URL: http://arxiv.org/abs/2408.13442v3
• Date: Sun, 31 Aug 2025 18:53:24 GMT
• Title: A Law of Next-Token Prediction in Large Language Models
• Authors: Hangfeng He, Weijie J. Su,
• Abstract summary: Large language models (LLMs) have been widely employed across various application domains.<n>We introduce a precise and quantitative law that governs the learning of contextualized token embeddings.<n>Our findings reveal that each layer contributes equally to enhancing prediction accuracy.
• Score: 26.240524947579118
• License: http://creativecommons.org/licenses/by/4.0/
• 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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