The widespread adoption of large language models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the misapplication of AI-generated text, necessitating increased vigilance. While these models are primarily trained for English, their extensive training on vast datasets covering almost the entire web, equips them with capabilities to perform well in numerous other languages. AI-Generated Text Detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by the emergence of techniques to bypass detection. In this paper, we report our investigation on AGTD for an indic language Hindi. Our major contributions are in four folds: i) examined 26 LLMs to evaluate their proficiency in generating Hindi text, ii) introducing the AI-generated news article in Hindi ($AG_{hi}$) dataset, iii) evaluated the effectiveness of five recently proposed AGTD techniques: ConDA, J-Guard, RADAR, RAIDAR and Intrinsic Dimension Estimation for detecting AI-generated Hindi text, iv) proposed Hindi AI Detectability Index ($ADI_{hi}$) which shows a spectrum to understand the evolving landscape of eloquence of AI-generated text in Hindi. We will make the codes and datasets available to encourage further research.

翻译：大型语言模型（LLMs）的广泛采用以及对多语言LLMs的认识，引发了人们对AI生成文本误用所带来的潜在风险与后果的担忧，这要求我们提高警惕。虽然这些模型主要针对英语进行训练，但它们在海量数据集（几乎涵盖整个互联网）上的广泛训练，使其具备了在众多其他语言中良好表现的能力。AI生成文本检测（AGTD）已成为一个在研究中立即受到关注的主题，一些初步方法已被提出，随后很快出现了规避检测的技术。本文报告了我们对一种印度语言——印地语的AGTD进行的调查。我们的主要贡献体现在四个方面：i) 考察了26个LLMs，评估其生成印地语文本的能力；ii) 引入了印地语AI生成新闻文章（$AG_{hi}$）数据集；iii) 评估了五种近期提出的AGTD技术（ConDA、J-Guard、RADAR、RAIDAR和内在维度估计）在检测印地语AI生成文本方面的有效性；iv) 提出了印地语AI可检测性指数（$ADI_{hi}$），该指数提供了一个理解印地语AI生成文本流畅度演变态势的谱系。我们将公开代码和数据集，以鼓励进一步的研究。