The rise of Generative AI has led to a surge in AI-generated reviews, often posing a serious threat to the credibility of online platforms. Reviews serve as the primary source of information about products and services. Authentic reviews play a vital role in consumer decision-making. The presence of fabricated content misleads consumers, undermines trust and facilitates potential fraud in digital marketplaces. This study focuses on detecting AI-generated product reviews in Tamil and Malayalam, two low-resource languages where research in this domain is relatively under-explored. We worked on a range of approaches - from traditional machine learning methods to advanced transformer-based models such as Indic-BERT, IndicSBERT, MuRIL, XLM-RoBERTa and MalayalamBERT. Our findings highlight the effectiveness of leveraging the state-of-the-art transformers in accurately identifying AI-generated content, demonstrating the potential in enhancing the detection of fake reviews in low-resource language settings.

本研究解决了低资源语言（如塔米尔语和马拉雅拉姆语）中AI生成产品评价检测的不足。我们采用从传统机器学习到先进的变换器模型（如Indic-BERT等）的多种方法，发现使用最先进的变换器能够有效识别AI生成的内容，进而提升低资源语言环境中的虚假评价检测能力。 

揭开面纱！德拉威语中AI生成产品评价的检测