Original scientific paper
https://doi.org/10.32985/ijeces.17.9.5
An Approach Based on Fine-tuning Small Language Models for Fake News Detection
Khac-Lap Phan
; Department of Information Technology, Quy Nhon University 170 An Duong Vuong, Quy Nhon Nam, Gia Lai, Vietnam
Quang-Vinh Pham
; Department of Data Science, TMA Tech Group Quy Nhon Nam, Gia Lai, Vietnam
Quang-Hung Le
; Department of Information Technology, Quy Nhon University 170 An Duong Vuong, Quy Nhon Nam, Gia Lai, Vietnam
*
* Corresponding author.
Abstract
Social networks have facilitated the rapid spread of fake news. This can negatively affect the society. Therefore, effective fake news detection is growing increasingly important. Recently, large language models (LLMs) have brought out impressive results. However, practical challenges remain because LLMs require significant computational resources. This paper explores the potential of high-performance small language models (SLMs) as an effective alternative in edge and low-resource settings. Particularly, we propose SLM-FND, a lightweight yet effective framework based on fine-tuning small language models. We evaluated state-of-the- art models, including DistilBERT, MiniLM, and ALBERT using both full fine-tuning and parameter-efficient fine-tuning methods on benchmark datasets. The results show that the proposed method achieves approximately 98.3% accuracy and F1-score values on balanced datasets. This indicates that SLMs yield competitive performance to LLM-based baselines, while requiring significantly fewer trainable parameters, shorter inference times, and lower hardware cost. Furthermore, parameter-efficient fine-tuning methods promote adaptability and minimize the computational cost. The code for the implementation of this work has been shared in the following GitHub repository (https://github.com/PhanKhacLapQNU/SLM-FND).
Keywords
Fake News Detection; Small Language Models; Parameter-Efficient Fine-Tuning; SLM-FND;
Hrčak ID:
352141
URI
Publication date:
14.10.2026.
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