@inproceedings{liu-poesio-2025-exploring,
    title = "Exploring the Usage of Knowledge Graphs in Identifying Human and {LLM}-Generated Fake Reviews",
    author = "Liu, Ming  and
      Poesio, Massimo",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.78/",
    pages = "674--681",
    abstract = "The emergence of large language models has led to an explosion of machine-generated fake reviews. Although distinguishing between human and LLM-generated fake reviews is an area of active research, progress is still needed. One aspect which makes current LLM-generated fake reviews easier to recognize is that LLMs{--}in particular the smaller ones{--}lack domain-related knowledge. The objective of this work is to investigate whether large language models can produce more realistic artificial reviews when supplemented with knowledge graph information, thus resulting in a more challenging training dataset for human and LLM-generated fake reviews detectors. We propose a method for generating fake reviews by providing knowledge graph information to a llama model, and used it to generate a large number of fake reviews which used to fine tune a state-of-the-art human and LLM-generated fake reviews detection system. Our results show that when knowledge graph information is provided as part of the input, the accuracy of the model is improved by 0.24{\%}. When the knowledge graph is used as an embedding layer and combined with the existing input embedding layer, the accuracy of the detection model is improved by 1.279{\%}."
}