LLMs Solution to Fake News, Disinformation, and Hoaxes: Llama 3 [70B]-based Hoax Detection and Counteraction System

LLMs Solution to Fake News, Disinformation, and Hoaxes: Llama 3 [70B]-based Hoax Detection and Counteraction System

Authors

DOI:

https://doi.org/10.56741/jnest.v4i02.952

Keywords:

Large Language Models (LLMs), Llama 3, Hoax Detection, Disinformation, Fake News, Digital Literacy

Abstract

In the digital age, hoaxes or false information are a significant challenge, as they can harm public comprehension, form inaccurate opinions, and endanger the health and safety of individuals. Artificial intelligence technology, particularly large language models (LLMs) like Llama 3, provides an innovative solution to these challenges. A sophisticated generative model with superior natural language processing capabilities, Llama 3 enables the effective detection and clarification of hoaxes. A dataset that is seven times larger than its antecedent, Llama 2, is utilized to train this model. The dataset has a token capacity of up to 128K and a context length of up to 8 K. By utilizing these capabilities, Llama 3 is capable of comprehending context, offering responses that are grounded in scientific data, and reducing response errors. Educational chatbots, interactive web platforms, and mobile applications that are based on Llama 3 can be implemented. This model effectively identifies and clarifies false information regarding cosmic rays that are purportedly hazardous through the presentation of pertinent scientific facts, as demonstrated by case studies. Llama 3's capabilities encompass its capacity to modify parameters to generate valid and pertinent responses. This renders it a critical instrument for bolstering community resilience to the dissemination of falsehoods, as well as digital literacy and awareness. Llama 3, which is open source, facilitates global collaboration in the development of a more secure and trustworthy information ecosystem.

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Author Biographies

Adi Jufriansah, Universitas Muhammadiyah Maumere

Adi Jufriansah is a lecturer at Physics Education, Universitas Muhammadiyah Maumere. His research areas are image/signal processing, biometrics, pattern recognition image analysis, machine learning deep learning and mathematical model. Several grants have been obtained at national and international levels from the International Astronomical Union (IAU). (email: saompu@gmail.com).

Yudhiakto Pramudya, Universitas Ahmad Dahlan

Yudhiakto Pramudya earned his doctoral degree in physics from Wesleyan University, USA, specializing in the field of superfluids. He currently works as a lecturer at Universitas Ahmad Dahlan in Yogyakarta, researching vibrations and waves. (email: yudhiakto.pramudya@pfis.uad.ac.id).

Azmi Khusnani, Universitas Muhammadiyah Maumere

Azmi Khusnani is a lecturer in the Physics Education Study Program at Universitas Muhammadiyah Maumere, Indonesia. Her current research focus is on earthquakes and disaster mitigation. She also has many publications in Scopus and accredited national journals. (email: husnaniazmi@gmail.com).

Edwin Ariesto Umbu Malahina, STIKOM Uyelindo Kupang

Edwin Ariesto Umbu Malahina is a lecturer at STIKOM Uyelindo Kupang, specializing in Mobile Computing, Generative Artificial Intelligence, Classification, and Software Engineering. He is actively involved in academic, research, and technological development activities, with a focus on applying intelligent solutions through mobile computing and AI technologies. (email: edwinariesto@gmail.com).

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Published

2025-05-09

How to Cite

Jufriansah, A., Pramudya, Y., Khusnani, A., & Malahina, E. A. U. (2025). LLMs Solution to Fake News, Disinformation, and Hoaxes: Llama 3 [70B]-based Hoax Detection and Counteraction System. Journal of Novel Engineering Science and Technology, 4(02), 57–62. https://doi.org/10.56741/jnest.v4i02.952

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