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arXiv — NLP / Computation & Language · · 3 min read

CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance

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Computer Science > Computation and Language

arXiv:2608.21462 (cs)
[Submitted on 20 Aug 2026]

Title:CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance

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Abstract:Due to the selection of their training data, large language models (LLMs) perform best on standard-language inputs from languages using the Latin alphabet with large speaker populations, while disadvantaging other language varieties. Nevertheless, they can also be a versatile tool for preserving precisely such endangered languages. But do they also possess the necessary creativity and capacity for abstraction to decode phonetically encoded language the same way humans do?
Comments: 4 pages, in English; 4 pages, in German (original); German version originally published in: Rüdian, S. (2026). Prompt-Engineering in Education (1st ed., pp. 39-42). Humboldt-Universität zu Berlin. this https URL
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2608.21462 [cs.CL]
  (or arXiv:2608.21462v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.21462
arXiv-issued DOI via DataCite

Submission history

From: Erik Thureck [view email]
[v1] Thu, 20 Aug 2026 19:48:15 UTC (91 KB)
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