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arXiv Machine Learning By Adnan Aboulala\^a

The Probabilistic Structure of Large Language Models

Read the original on arXiv Machine Learning →

The paper offers a unified probabilistic framework for large language models, describing them as probability measures over token sequences defined by autoregressive conditional distributions. Training is cast as maximum‑likelihood estimation solved via stochastic gradient methods, while generation is treated as sequential simulation of the resulting stochastic process. It also explores how the asymmetry of the Kullback–Leibler divergence relates to hallucination and the distinction between plausibility and truth, and extends the perspective to diffusion models that generate data by simulating a reverse‑time stochastic process.

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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