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arXiv Machine Learning By Ricardo Baptista, Andrew Stuart, Son Tran

Large Language Models: A Mathematical Formulation

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The article presents a mathematical framework for large language models (LLMs), detailing how text sequences are encoded into tokens, how next‑token prediction architectures are defined, and how these models are trained and deployed for tasks such as summarization, recommendation, software writing, and quantitative problem solving. It emphasizes that the framework relies on basic concepts from information theory, probability, and optimization, yet captures the complex algorithmic structure responsible for LLMs’ empirical successes. The authors argue that this formalism enables the study of accuracy, efficiency, and robustness, and points toward new methodological developments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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