@inproceedings{sabri-etal-2025-detecting,
title = "Detecting Fake News in the Era of Language Models",
author = "Sabri, Muhammad Irfan Fikri and
Hettiarachchi, Hansi and
Ranasinghe, Tharindu",
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-1.119/",
pages = "1036--1043",
abstract = "The proliferation of fake news has been amplified by the advent of large language models (LLMs), which can generate highly realistic and scalable misinformation. While prior studies have focused primarily on detecting human-generated fake news, the efficacy of current models against LLM-generated content remains underexplored. We address this gap by compiling a novel dataset combining public and LLM-generated fake news, redefining detection as a ternary classification task (real, human-generated fake, LLM-generated fake), and evaluating eight diverse classification models, including traditional machine learning, fine-tuned transformers, and few-shot prompted LLMs. Our findings highlight the strengths and limitations of these models in detecting evolving LLM-generated fake news, offering insights for future detection strategies."
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<abstract>The proliferation of fake news has been amplified by the advent of large language models (LLMs), which can generate highly realistic and scalable misinformation. While prior studies have focused primarily on detecting human-generated fake news, the efficacy of current models against LLM-generated content remains underexplored. We address this gap by compiling a novel dataset combining public and LLM-generated fake news, redefining detection as a ternary classification task (real, human-generated fake, LLM-generated fake), and evaluating eight diverse classification models, including traditional machine learning, fine-tuned transformers, and few-shot prompted LLMs. Our findings highlight the strengths and limitations of these models in detecting evolving LLM-generated fake news, offering insights for future detection strategies.</abstract>
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%0 Conference Proceedings
%T Detecting Fake News in the Era of Language Models
%A Sabri, Muhammad Irfan Fikri
%A Hettiarachchi, Hansi
%A Ranasinghe, Tharindu
%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 sabri-etal-2025-detecting
%X The proliferation of fake news has been amplified by the advent of large language models (LLMs), which can generate highly realistic and scalable misinformation. While prior studies have focused primarily on detecting human-generated fake news, the efficacy of current models against LLM-generated content remains underexplored. We address this gap by compiling a novel dataset combining public and LLM-generated fake news, redefining detection as a ternary classification task (real, human-generated fake, LLM-generated fake), and evaluating eight diverse classification models, including traditional machine learning, fine-tuned transformers, and few-shot prompted LLMs. Our findings highlight the strengths and limitations of these models in detecting evolving LLM-generated fake news, offering insights for future detection strategies.
%U https://aclanthology.org/2025.ranlp-1.119/
%P 1036-1043
Markdown (Informal)
[Detecting Fake News in the Era of Language Models](https://aclanthology.org/2025.ranlp-1.119/) (Sabri et al., RANLP 2025)
ACL
- Muhammad Irfan Fikri Sabri, Hansi Hettiarachchi, and Tharindu Ranasinghe. 2025. Detecting Fake News in the Era of Language Models. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 1036–1043, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.