@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://aclanthology.org/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{\%}."
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<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%.</abstract>
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%0 Conference Proceedings
%T Exploring the Usage of Knowledge Graphs in Identifying Human and LLM-Generated Fake Reviews
%A Liu, Ming
%A Poesio, Massimo
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F liu-poesio-2025-exploring
%X 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%.
%U https://aclanthology.org/2025.ranlp-1.78/
%P 674-681
Markdown (Informal)
[Exploring the Usage of Knowledge Graphs in Identifying Human and LLM-Generated Fake Reviews](https://aclanthology.org/2025.ranlp-1.78/) (Liu & Poesio, RANLP 2025)
ACL