Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach
The growing spread of misinformation in digital media highlights the need for reliable fake news detection systems, yet progress in under-resourced languages such as Bangla is limited by small and imbalanced datasets. This study investigates whether Large Language Model (LLM) based augmentation can effectively address this limitation and improve Bangla fake news classification. Existing datasets remain valuable but highly imbalanced, limiting model performance, and LLM based augmentation for Bangla has been scarcely explored. To fill this gap, we propose a systematic augmentation framework that generates synthetic Bangla news articles using the instruction tuned Gemma 3 27B IT model, supp...
| Authors | Ahmed Alfey Sani, Kazi Akib Zaoad, Shefayat E Shams Adib et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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