mlsanityData to 14 Oct 2026

Unmask It! AI-Generated Product Review Detection in Dravidian Languages

arXiv 2503.09289 ↗First posted 12 Mar 2025cs.CL cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 12 Mar 2025.

Builds on

5 of 13 references.

References · 5

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  2. 2021MuRIL: Multilingual Representations for Indian Languagescited by 179
  3. 2022L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languagescited by 35
  4. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  5. 2023L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERTcited by 15

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