Linguistic Monoculture in LLM-Assisted Language Use

2026-07-29

AI Summary

This study addresses the risk that large language model (LLM)-assisted writing may induce linguistic convergence across user populations, thereby eroding linguistic diversity. It formalizes linguistic homogenization as a negative externality and introduces a co-evolutionary framework modeling the joint dynamics of authors’ and LLMs’ linguistic feature distributions. Integrating dynamical systems, game-theoretic utility optimization, and synthetic simulations, the analysis examines the effects of shared models, recursive feedback loops, and personalization mechanisms. The findings reveal that rational individual trade-offs between clarity and stylistic distinctiveness can lead to socially suboptimal convergence; shared models intensify this homogenization, while recursive feedback merely shifts linguistic norms without altering dispersion. In contrast, personalization mechanisms sustain a non-zero diversity equilibrium and enable quantification of the “monoculture cost.”

Abstract

Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text. Although such assistance can improve clarity and help authors meet institutional expectations, widespread reliance on shared models may reduce population-level variation in linguistic form, a phenomenon we refer to as linguistic monoculture. We develop a mathematical framework in which authors and LLMs are represented as distributions over linguistic features and coevolve through repeated interaction. We analyze three interaction mechanisms: a shared model with a fixed linguistic distribution, a shared model recursively updated from author outputs, and personalized models updated through author-specific and population-level feedback. We characterize the resulting equilibria and convergence rates, showing that, shared models can drive authors toward a common norm, recursive feedback relocates the shared norm without altering pairwise spread under common conformity, and personalization can preserve a family of distinct author-model equilibria with nonzero linguistic diversity. We then endogenize conformity as a strategic choice trading off private benefits from clarity, legibility, and perceived fluency against distinctive style. Within this utility model, individually rational authors may conform more than is socially optimal because they do not internalize the value their distinctiveness provides to others, creating a negative externality and a price of monoculture that is finite for each fixed instance but can grow without bound when distinctiveness dominates authenticity. Synthetic simulations illustrate how fixed shared assistance, recursive feedback, and personalization produce different long-run diversity outcomes.

Problem

• linguistic monoculture
• large language models
• language diversity
• conformity
• author-model interaction

Innovation

• linguistic monoculture
• coevolution
• personalization
• conformity externality
• language diversity