BanglaMamba: Exploring State Space Models for Bangla Fake News Detection

arXiv:2608.25190 · cs.CL, cs.LG · Submitted 2026-08-25 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Transformer — 35× in this paper · explained in 2 episodes
• AUC — 14× in this paper · explained in 2 episodes
• Generalization — 12× in this paper · explained in 13 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• F1 Score — 4× in this paper · explained in 3 episodes
• Natural Language Processing — 4× in this paper
• Mamba-based State Space Model — 3× in this paper
• State Space Models (SSMs) — 3× in this paper · explained in 4 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Expected Calibration Error — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Matthews Correlation Coefficient — 1× in this paper
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Efficiently Modeling Long Sequences with Structured State Spaces
• Training Compute-Optimal Large Language Models

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