@inproceedings{agrahari-etal-2025-really,
    title = "Can You Really Trust That Review? {P}roto{F}ew{R}o{BERT}a and {D}etect{AIR}ev: A Prototypical Few-Shot Method and Multi-Domain Benchmark for Detecting {AI}-Generated Reviews",
    author = "Agrahari, Shifali  and
      Kumar, Sujit  and
      Sanasam, Ranbir Singh",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.132/",
    pages = "2118--2140",
    ISBN = "979-8-89176-303-6",
    abstract = "Synthetic reviews mislead users and erode trust in online marketplaces, and the advent of Large Language Models (LLMs) makes detecting such AI-generated content increasingly challenging due to their human-like fluency and coherence. In the literature, LLM-generated review detection datasets are limited to one or a few domains, with reviews generated by only a few LLMs. Consequently, datasets are limited in diversity in terms of both domain coverage and review generation styles. Models trained on such datasets generalize poorly, lacking cross-model adaptation and struggling to detect diverse LLM-generated reviews in real-world, open-domain scenarios. To address this, we introduce DetectAIRev, a benchmark dataset for AI-generated review detection that includes human-written reviews from diverse domains and AI-generated reviews generated by various categories of LLMs. We evaluate the quality and reliability of the proposed dataset through several ablation studies and human evaluations. Furthermore, we propose an AI-generated text detection method ProtoFewRoBERTa, a few-shot framework that combines prototypical networks with RoBERTa embeddings, which learn discriminative features across multiple LLMs and human-written text using only a few labeled examples per class to discriminate between LLMs as the author for text author detection. We conduct our experiments on the DetectAIRev and a publicly available benchmark dataset. Our experimental results suggest that our proposed methods outperform the state-of-the-art baseline models in detecting AI-generated reviews and text detection."
}