Year: 2025
Type: conference-paper
Abstract: 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, labelbased 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 targetlanguage 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 lowresource scenarios with smaller models.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416036701