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

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--> [Submitted on 20 Aug 2026] Title:CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance View a PDF of the paper titled CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance, by Erik Thureck and 1 other authors View PDF HTML (experimental) 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) Full-text links: Access Paper: View a PDF of the paper titled CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance, by Erik Thureck and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-08 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)