VLQA: The First Comprehensive, Large, and High-Quality Vietnamese Dataset for Legal Question Answering

TL;DR

This paper introduces VLQA, a large, high-quality Vietnamese legal dataset, and demonstrates its usefulness for legal question answering and information retrieval tasks using state-of-the-art models.

Contribution

The paper presents the first comprehensive Vietnamese legal dataset, addressing resource scarcity and enabling improved legal NLP applications in Vietnamese.

Findings

VLQA improves legal question answering performance in Vietnamese.

State-of-the-art models benefit from the VLQA dataset.

The dataset enables better legal information retrieval in Vietnamese.

Abstract

The advent of large language models (LLMs) has led to significant achievements in various domains, including legal text processing. Leveraging LLMs for legal tasks is a natural evolution and an increasingly compelling choice. However, their capabilities are often portrayed as greater than they truly are. Despite the progress, we are still far from the ultimate goal of fully automating legal tasks using artificial intelligence (AI) and natural language processing (NLP). Moreover, legal systems are deeply domain-specific and exhibit substantial variation across different countries and languages. The need for building legal text processing applications for different natural languages is, therefore, large and urgent. However, there is a big challenge for legal NLP in low-resource languages such as Vietnamese due to the scarcity of resources and annotated data. The need for labeled legal corpora for supervised training, validation, and supervised fine-tuning is critical. In this paper, we introduce the VLQA dataset, a comprehensive and high-quality resource tailored for the Vietnamese legal domain. We also conduct a comprehensive statistical analysis of the dataset and evaluate its effectiveness through experiments with state-of-the-art models on legal information retrieval and question-answering tasks.