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masood, Dina, Faili, Heshaam. (1404). Neuro-Symbolic Claim Detection for Arabic: Integrating Graph Neural Networks with Efficiently Fine-Tuned Small Language Models. نشریه مهندسی عمران امیرکبیر, (), -. doi: 10.22060/miscj.2025.24744.5431
Dina falah masood; Heshaam Faili. "Neuro-Symbolic Claim Detection for Arabic: Integrating Graph Neural Networks with Efficiently Fine-Tuned Small Language Models". نشریه مهندسی عمران امیرکبیر, , , 1404, -. doi: 10.22060/miscj.2025.24744.5431
masood, Dina, Faili, Heshaam. (1404). 'Neuro-Symbolic Claim Detection for Arabic: Integrating Graph Neural Networks with Efficiently Fine-Tuned Small Language Models', نشریه مهندسی عمران امیرکبیر, (), pp. -. doi: 10.22060/miscj.2025.24744.5431
masood, Dina, Faili, Heshaam. Neuro-Symbolic Claim Detection for Arabic: Integrating Graph Neural Networks with Efficiently Fine-Tuned Small Language Models. نشریه مهندسی عمران امیرکبیر, 1404; (): -. doi: 10.22060/miscj.2025.24744.5431

Neuro-Symbolic Claim Detection for Arabic: Integrating Graph Neural Networks with Efficiently Fine-Tuned Small Language Models

AUT Journal of Modeling and Simulation
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 16 آذر 1404
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/miscj.2025.24744.5431
نویسندگان
Dina falah masood1؛ Heshaam Faili* 2
1Computer engineering department, college of Alborz, University of Tehran, Tehran, Iran
2electrical and computer Engineering, university of Tehran, Tehran, Iran
چکیده
The rise in online information has increased the spread of misinformation and disinformation, which makes claim detection important for the reliability of digital content. Claim detection refers to the problem of determining whether a factual statement is real or fake. While there has been great advancement in high-resource languages, Arabic is particularly challenging due to its special characteristics and the low availability of annotated resources. In this paper, we make use of the Arabic News Stance (ANS) corpus and develop a multi-stage framework to help tackle these problems. Our method effectively models the structural interplay between claims and words with a carefully heterogeneous bipartite graph and the use of advanced Graph Neural Networks (GNNs). In parallel, we used two Small-scale Language Models (SLMs) in this domain, namely LLaMA-3.2-1B and Qwen2.5-3B, and demonstrated that together with QLoRA-based fine-tuning, these lightweight models can be as effective as or even better than heavier baselines. To this point, we suggested a novel neuro-symbolic fusion paradigm that unifies structural reasoning with linguistic prior information. By integrating the outputs from a Graph Convolutional Network (GCN) with the fine-tuned Qwen2.5-3B logits, we achieved the state-of-the-art performance on the ANS dataset with an accuracy of 0.732 and an F1-score of 0.717. This framework shows that the combined results of structural graph reasoning and SLMs lead to accurate and computationally efficient claim detection. This work not only enhances the detection of Arabic misinformation but also provides a scalable solution for low-resource languages.
کلیدواژه‌ها
Claim detection؛ Arabic؛ Graph Neural Networks؛ Small-scale Language Models؛ Supervised fine tuning
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