A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages

A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages

Stať ve sborníku mimo WoS a Scopus

Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. However, a comparison of various generation strategies for low-resource language settings is lacking. While various prompting strategies have been proposed-such as demonstrations, label-based summaries, and self-revision-their comparative effectiveness remains unclear, especially for low-resource languages. In this paper, we systematically evaluate the performance of these generation strategies and their combinations across 11 typologically diverse languages, including several extremely low-resource ones. Using three NLP tasks and four open-source LLMs, we assess downstream model performance on generated versus gold-standard data. Our results show that strategic combinations of generation methods - particularly target-language demonstrations with LLM-based revisions - yield strong performance, narrowing the gap with real data to as little as 5% in some settings. We also find that smart prompting techniques can reduce the advantage of larger LLMs, highlighting efficient generation strategies for synthetic data generation in low-resource scenarios with smaller models

Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. However, a comparison of various generation strategies for low-resource language settings is lacking. While various prompting strategies have been proposed-such as demonstrations, label-based summaries, and self-revision-their comparative effectiveness remains unclear, especially for low-resource languages. In this paper, we systematically evaluate the performance of these generation strategies and their combinations across 11 typologically diverse languages, including several extremely low-resource ones. Using three NLP tasks and four open-source LLMs, we assess downstream model performance on generated versus gold-standard data. Our results show that strategic combinations of generation methods - particularly target-language demonstrations with LLM-based revisions - yield strong performance, narrowing the gap with real data to as little as 5% in some settings. We also find that smart prompting techniques can reduce the advantage of larger LLMs, highlighting efficient generation strategies for synthetic data generation in low-resource scenarios with smaller models

multilingual evaluation, less-resourced languages, model analysis, synthetic data generation

multilingual evaluation, less-resourced languages, model analysis, synthetic data generation

ANIKINA, T.; ČEGIŇ, J.; ŠIMKO, J.; OSTERMANN, S.

2026

04.11.2025

Association for Computational Linguistics

Suzhou, China

979-8-89176-332-6

8293

8314

22

https://aclanthology.org/2025.emnlp-main.418/