Vietnamese AI Generated Text Detection

TL;DR

This paper introduces ViDetect, a Vietnamese dataset for AI-generated text detection, and evaluates multiple models to identify the most effective methods in distinguishing human from AI-written Vietnamese text.

Contribution

The study provides a new Vietnamese dataset for AI text detection and benchmarks several state-of-the-art models, demonstrating their effectiveness in this language context.

Findings

ViT5 and PhoBERT perform best on the dataset.

The dataset includes 6,800 Vietnamese essays with balanced human and AI-generated samples.

Results highlight the adaptability of existing models for Vietnamese AI text detection.

Abstract

In recent years, Large Language Models (LLMs) have become integrated into our daily lives, serving as invaluable assistants in completing tasks. Widely embraced by users, the abuse of LLMs is inevitable, particularly in using them to generate text content for various purposes, leading to difficulties in distinguishing between text generated by LLMs and that written by humans. In this study, we present a dataset named ViDetect, comprising 6.800 samples of Vietnamese essay, with 3.400 samples authored by humans and the remainder generated by LLMs, serving the purpose of detecting text generated by AI. We conducted evaluations using state-of-the-art methods, including ViT5, BartPho, PhoBERT, mDeberta V3, and mBERT. These results contribute not only to the growing body of research on detecting text generated by AI but also demonstrate the adaptability and effectiveness of different methods in the Vietnamese language context. This research lays the foundation for future advancements in AI-generated text detection and provides valuable insights for researchers in the field of natural language processing.