Evaluating Large Language Model Capability in Vietnamese Fact-Checking Data Generation

TL;DR

This paper investigates the use of Large Language Models for automatic Vietnamese fact-checking data generation, highlighting improvements through fine-tuning but still falling short of human data quality.

Contribution

It introduces an automatic data construction process for Vietnamese fact-checking and evaluates methods to enhance data quality generated by LLMs.

Findings

Fine-tuning improves data quality significantly.

LLMs still lag behind human-generated data.

Manual and performance evaluations validate the improvements.

Abstract

Large Language Models (LLMs), with gradually improving reading comprehension and reasoning capabilities, are being applied to a range of complex language tasks, including the automatic generation of language data for various purposes. However, research on applying LLMs for automatic data generation in low-resource languages like Vietnamese is still underdeveloped and lacks comprehensive evaluation. In this paper, we explore the use of LLMs for automatic data generation for the Vietnamese fact-checking task, which faces significant data limitations. Specifically, we focus on fact-checking data where claims are synthesized from multiple evidence sentences to assess the information synthesis capabilities of LLMs. We develop an automatic data construction process using simple prompt techniques on LLMs and explore several methods to improve the quality of the generated data. To evaluate the quality of the data generated by LLMs, we conduct both manual quality assessments and performance evaluations using language models. Experimental results and manual evaluations illustrate that while the quality of the generated data has significantly improved through fine-tuning techniques, LLMs still cannot match the data quality produced by humans.