arXiv Computation and Language September 1, 2026 By Lester James V. Miranda, Ivan Vuli\'c, Anna Korhonen

Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation

Read the original on arXiv Computation and Language →

The paper investigates how to choose language models (teachers) for generating multilingual synthetic data used to fine‑tune smaller student models. By evaluating 10 teacher models across six diverse languages and training 240 students, the authors find that teacher effectiveness is not driven by model size but by data qualities such as prompt diversity, length, and fluency, which explain most of the variance in student performance. Practical guidelines are offered, including matching teacher and student families and using translated prompts to improve outcomes for low‑resource languages.

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