JAPAGEN: Efficient Few/Zero-shot Learning via Japanese Training Dataset Generation with LLM

TL;DR

JAPAGEN introduces a novel approach using LLM-generated Japanese training data to enhance few-shot and zero-shot learning for compact models, achieving competitive classification performance.

Contribution

The paper presents JAPAGEN, a new method for generating Japanese training data with LLMs to improve low-resource language task performance.

Findings

JAPAGEN performs well on Japanese classification tasks.

Synthesized data enables effective training of compact models.

Competitive results compared to LLM prompting strategies.

Abstract

Recently some studies have highlighted the potential of Large Language Models (LLMs) as effective generators of supervised training data, offering advantages such as enhanced inference efficiency and reduced costs associated with data collection. However, these studies have predominantly focused on English language tasks. In this paper, we address the fundamental research question: Can LLMs serve as proficient training data generators for other language tasks? Specifically, we leverage LLMs to synthesize supervised training data under few-shot and zero-shot learning scenarios across six diverse Japanese downstream tasks. Subsequently, we utilize this synthesized data to train compact models (e.g., BERT). This novel methodology is termed JAPAGEN. Our experimental findings underscore that JAPAGEN achieves robust performance in classification tasks that necessitate formal text inputs, demonstrating competitive results compared to conventional LLM prompting strategies.