针对越南语生成文本，提出高效零样本检测方法。
VietBinoculars: A Zero-Shot Approach for Detecting Vietnamese LLM-Generated Text
- 基于优化全局阈值的零样本检测框架
- 在多个跨域数据集上准确率、F1和AUC均超99%
- 适合需要快速部署的中文/东南亚语言检测场景
基于Transformer架构的大语言模型（LLMs）快速发展，带来了一个关键挑战：区分人类写作与大模型生成文本。随着生成内容日益复杂且接近人类写作，传统检测方法效果下降，尤其在新型模型不断涌现的背景下。本文提出VietBinoculars，是对Binoculars方法的改进，通过优化全局阈值以提升对越南语生成文本的检测能力。我们构建了新的越南语AI生成数据集，用于确定最优阈值并建立基准。实验结果表明，VietBinoculars在多个跨域数据集上的准确率、F1分数和AUC均超过99%，优于原始Binoculars模型、传统方法及当前最先进的技术（包括ZeroGPT和DetectGPT等商用工具），特别是在特殊提示策略下表现更优。
原文摘要 · Abstract (English)
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.
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