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The Probabilistic Structure of Large Language Models
Author1, Author2, Author3, Author4, Author5
probabilistic modelinglanguage modelstext generationstochastic processes
2609.25134
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1h ago70%
Abstract
This paper presents a probabilistic perspective on large language models (LLMs), integrating various tools typically treated separately in the literature.
Reality Card
Core Claim
The paper successfully formulates the training of LLMs as a maximum-likelihood estimation problem and explores the implications of Kullback-Leibler divergence in text generation.
Method / Result
The examination of the asymmetry of the Kullback-Leibler divergence in relation to hallucination and statistical plausibility.
Limitations
The paper does not address potential challenges in reproducing the stochastic processes described.
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