VietBinoculars: A Zero-Shot Approach for Detecting Vietnamese LLM-Generated Text
This addresses the challenge of detecting AI-generated text in Vietnamese, which is incremental as it adapts an existing method to a specific language.
The study tackled the problem of distinguishing human-written from LLM-generated text in Vietnamese by proposing VietBinoculars, an adaptation of the Binoculars method with optimized thresholds, achieving over 99% accuracy, F1-score, and AUC on out-of-domain datasets.
The rapid development research of Large Language Models (LLMs) based on transformer architectures raises key challenges, one of them being the task of distinguishing between human-written text and LLM-generated text. As LLM-generated textual content, becomes increasingly complex over time, and resembles human writing, traditional detection methods are proving less effective, especially as the number and diversity of LLMs continue to grow with new models and versions being released at a rapid pace. This study proposes VietBinoculars, an adaptation of the Binoculars method with optimized global thresholds, to enhance the detection of Vietnamese LLM-generated text. We have constructed new Vietnamese AI-generated datasets to determine the optimal thresholds for VietBinoculars and to enable benchmarking. The results from our experiments show results show that VietBinoculars achieves over 99\% in all two domains of accuracy, F1-score, and AUC on multiple out-of-domain datasets. It outperforms the original Binoculars model, traditional detection methods, and other state-of-the-art approaches, including commercial tools such as ZeroGPT and DetectGPT, especially under specially modified prompting strategies.