CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance
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Abstract (arXiv)
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?
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References · 9
- 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
- 2023Quantifying the Dialect Gap and its Correlates Across Languagescited by 33
- 2024Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Taskscited by 22
- 2024Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMscited by 21
- 2024Evaluating Dialect Robustness of Language Models via Conversation Understandingcited by 14
- 2025Large Language Models Discriminate Against Speakers of German Dialectscited by 13
- 2024Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attackscited by 10
- 2024Exploring the Role of Transliteration in In-Context Learning for Low-resource Languages Written in Non-Latin Scriptscited by 8
- 2025IYKYK: Using language models to decode extremist cryptolectscited by 3
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