Analysis of Numerical Localisation in LLM Translations

Abstract:The work of Tang et. al. (2025) on numerical translation is extended by analysing the capability of five large language models (LLMs) for the localisation of times, numbers, and dates instead of translation. Models were selected that could be loaded onto and run on commodity hardware and a baseline quality for each mode is computed, then three different strategies to improve on that accuracy were tested. In contrast to Tang et. al., it was discovered that on the tested LLMs, embedding the localisation principles into the prompt context provided a statistically significant improvement in accuracy compared to direct translation or the alternative strategies.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2608.05232 [cs.CL]

(or arXiv:2608.05232v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.05232 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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