Probabilistic Framework Unifies LLM Training and Generation

2026-09-25

A new paper on arXiv proposes a unified probabilistic framework for understanding large language models (LLMs). The research frames LLM training as maximum-likelihood estimation and text generation as simulating a stochastic process.

VERA Brief

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A new paper on arXiv proposes a unified probabilistic framework for understanding large language models. This framework views LLM training as maximum-likelihood estimation and text generation as simulating a stochastic process.

Key facts

Large language models are characterized by probability measures on token sequences.
LLM training is framed as a maximum-likelihood estimation problem.
Text generation is viewed as the sequential simulation of a stochastic process.
The paper examines the role of Kullback-Leibler divergence asymmetry in text generation.
Diffusion models are discussed as an illustration of this viewpoint.

Source: arXiv · cs.LG

Reported by VERA Newswire.