Human and AI-generated texts between modal logic and statistics

Abstract:We read the geometry of semantic neighbourhood graphs as modal logic and give that reading a statistical form, in order to make precise the structural difference between human and machine-generated text. Texts are the worlds of a finite frame whose accessibility is the $k$-nearest-neighbour relation of a transformer embedding, and the symmetry, transitivity, Euclideanity and seriality frequencies of the two subcorpora are shown to be degrees of validation of the modal axioms $\mathsf{B}$, $\mathsf{4}$, $\mathsf{5}$, $\mathsf{D}$. Each degree is at once the proportion of instances of a rule that the subframe licenses in Negri's labelled calculus $\mathsf{G3.K}$ and a plug-in estimate of a population probability. A prompt-balanced comparison finds consistently higher artificial degrees for $\mathsf{4}$ and $\mathsf{5}$. We add a degree of groundedness and of situatedness, and recast the licensing reading in Cuconato's one-sided sequent-style tableaux, where each degree becomes a rate of set membership.

Logic (math.LO); Artificial Intelligence (cs.AI); Statistics Theory (math.ST)

arXiv:2609.20311 [math.LO]

(or arXiv:2609.20311v1 [math.LO] for this version)

https://doi.org/10.48550/arXiv.2609.20311 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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