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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0198568" target="_blank" >RIV/00216305:26230/26:0198568 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2025.emnlp-main.418/" target="_blank" >https://aclanthology.org/2025.emnlp-main.418/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2025.emnlp-main.418" target="_blank" >10.18653/v1/2025.emnlp-main.418</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů