Are Language Models Script-Aware?

Title:Are Language Models Script-Aware?

Abstract:Language models frequently generate outputs in unintended languages or scripts, a phenomenon known as off-target generation. While existing research has focused on language selection, the dimension of script knowledge remains understudied: before any linguistic understanding can occur, users must recognize the graphic symbols in a model's response. We investigate whether Small and Large Language Models (SLMs and LLMs) possess script knowledge by testing them on multi-scriptic languages. Through two complementary experiments, we evaluate whether models (1) adapt their output script to match the input, and (2) follow explicit instructions to generate text in a specified script. The models we tested demonstrate substantial script knowledge: they all achieve a near-perfect Latin script fidelity (more than 98%) and follow script instructions with high frequency. Nevertheless, we notice differences between LLMs and SLMs, with higher scores for LLMs including for non-standard script combinations.

Computation and Language (cs.CL)

arXiv:2610.08037 [cs.CL]

(or arXiv:2610.08037v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.08037 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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