Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties

arXiv:2603.25489 · cs.CL · Submitted 2026-03-26 · Read on arXiv

cs.CL

Submitted: 2026-03-26

Updated: 2026-08-27

Comments: Findings of EMNLP 2026

Code: https://github.com/ZurichNLP/romansh_mt_eval

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 27× in this paper · explained in 1 episode
• Data Augmentation — 27× in this paper · explained in 7 episodes
• Benchmark — 17× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Rumantsch Grischun — 5× in this paper
• Low-Resource Languages — 4× in this paper · explained in 4 episodes
• Tokenization — 4× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
• Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation
• Robust Language Identification for Romansh Varieties
• Omnilingual MT: Machine Translation for 1,600 Languages

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