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An enriched category theory of language: from syntax to semantics
In this paper, we propose a mathematical framework for passing from probability distributions on extensions of given texts, such as the ones learned by today's large language models, to an enriched category containing semantic information.
Language Modeling with Reduced Densities
We answer this by constructing a functor from our enriched category of text to a particular enriched category of reduced density operators.
Language as a matrix product state
We propose a statistical model for natural language that begins by considering language as a monoid, then representing it in complex matrices with a compatible translation invariant probability measure.
Tensor network language model
We propose a new statistical model suitable for machine learning of systems with long distance correlations such as natural languages.
