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This research investigates whether Large Language Models can decode phonetically encoded secret languages, such as CyrillicQA, by leveraging their capacity for abstraction rather than relying on standard training data distributions. The study addresses a critical gap in AI inclusivity by testing if models can adapt to non-standard linguistic inputs that disadvantage minority language varieties while potentially aiding in the preservation of endangered tongues.

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

arXivAugust 25, 20261 min read

arXiv:2608.21462v1 Announce Type: cross 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?

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?

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

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

arXiv:2608.21462 [cs.CL]

(or arXiv:2608.21462v1 [cs.CL] for this version)

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