arXiv (All)
open access
CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance
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 pre
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance#920410
- The Voice and Speech Processing within Language Technology Applications: Perspective of the Russian Data Protection Law#461833
- A Sequence Sensitive Model of Encoding Precision#313355
- On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models#1122567
- EADC: Evaluation of Advanced and Deep-level Compliance in Large Language Models#1062076
Record · ID 924644
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive