The Latin Substrate: How Language Models Represent and Mediate Script Choice

Fuente: arXiv
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Auteurs principaux: Gurgurov, Daniil, Saji, Alan, Trinley, Katharina, van Genabith, Josef, Ostermann, Simon
Format: Preprint
Publié: 2026
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author Gurgurov, Daniil
Saji, Alan
Trinley, Katharina
van Genabith, Josef
Ostermann, Simon
author_facet Gurgurov, Daniil
Saji, Alan
Trinley, Katharina
van Genabith, Josef
Ostermann, Simon
contents Many languages are written in multiple scripts, requiring large language models (LLMs) to generate equivalent linguistic content in distinct orthographic forms. While prior work suggests that LLMs route information through shared latent representations, how they internally mediate script variation remains poorly understood. We study this question by first examining per-layer output distributions with the logit lens, which reveals consistent latent romanization during transliteration, and then through representational and mechanistic analyses of script generation. At the representational level, we show that scripts of the same language become increasingly separable across layers and that a simple linear steering direction can flip a model's output script while largely maintaining semantic content. The vector generalizes asymmetrically to writing systems unseen during construction, flipping non-Latin output to Latin reliably, but mapping Latin output into varied non-Latin scripts. At the mechanistic level, we localize a small set of late-layer attention heads that causally mediate script choice. These heads transfer across unrelated languages and writing systems, suggesting that script routing is implemented by language-agnostic components. Across both analyses, we observe a consistent directional asymmetry: non-Latin output is produced by a compact, identifiable gate, while Latin-script output emerges from diffuse contributions across the network. Collectively, our findings hint that LLMs organize script variation around shared latent representations while exhibiting a privileged substrate toward Latin script.
format Preprint
id arxiv_https___arxiv_org_abs_2605_31363
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle The Latin Substrate: How Language Models Represent and Mediate Script Choice
Gurgurov, Daniil
Saji, Alan
Trinley, Katharina
van Genabith, Josef
Ostermann, Simon
Computation and Language
Many languages are written in multiple scripts, requiring large language models (LLMs) to generate equivalent linguistic content in distinct orthographic forms. While prior work suggests that LLMs route information through shared latent representations, how they internally mediate script variation remains poorly understood. We study this question by first examining per-layer output distributions with the logit lens, which reveals consistent latent romanization during transliteration, and then through representational and mechanistic analyses of script generation. At the representational level, we show that scripts of the same language become increasingly separable across layers and that a simple linear steering direction can flip a model's output script while largely maintaining semantic content. The vector generalizes asymmetrically to writing systems unseen during construction, flipping non-Latin output to Latin reliably, but mapping Latin output into varied non-Latin scripts. At the mechanistic level, we localize a small set of late-layer attention heads that causally mediate script choice. These heads transfer across unrelated languages and writing systems, suggesting that script routing is implemented by language-agnostic components. Across both analyses, we observe a consistent directional asymmetry: non-Latin output is produced by a compact, identifiable gate, while Latin-script output emerges from diffuse contributions across the network. Collectively, our findings hint that LLMs organize script variation around shared latent representations while exhibiting a privileged substrate toward Latin script.
title The Latin Substrate: How Language Models Represent and Mediate Script Choice
topic Computation and Language
url https://arxiv.org/abs/2605.31363