BanglaMamba: Exploring State Space Models for Bangla Fake News Detection
BanglaMamba introduces a Mamba-based State Space Model for Bangla fake news detection, offering a computationally efficient alternative to Transformer-based architectures BanglaBERT achieves the highest Macro-F1 score (0.9260), but BanglaMamba (0.9029) performs comparably to a from-scratch CustomBERT (0.9057) BanglaMamba delivers approximately 2.2× higher inference throughput and 49% lower peak GPU memory usage compared to BERT-based models Cross-dataset evaluation reveals BanglaBERT generalizes
Analysis
TL;DR
- BanglaMamba introduces a Mamba-based State Space Model for Bangla fake news detection, offering a computationally efficient alternative to Transformer-based architectures
- BanglaBERT achieves the highest Macro-F1 score (0.9260), but BanglaMamba (0.9029) performs comparably to a from-scratch CustomBERT (0.9057)
- BanglaMamba delivers approximately 2.2× higher inference throughput and 49% lower peak GPU memory usage compared to BERT-based models
- Cross-dataset evaluation reveals BanglaBERT generalizes better to external datasets, underscoring the value of large-scale pretraining
- Mamba-based SSMs prove to be a viable, resource-efficient option for low-resource language NLP tasks like Bangla fake news detection
Why It Matters
This work addresses a critical gap in efficient NLP for low-resource languages, demonstrating that State Space Models can compete with Transformers while significantly reducing computational costs. For practitioners deploying models in resource-constrained environments—particularly in regions where Bangla is spoken—BanglaMamba offers a practical alternative that balances accuracy with inference efficiency.
Technical Details
- Architecture: BanglaMamba is built on Mamba-based State Space Models (SSMs), which provide linear computational complexity for sequence processing, contrasting with the quadratic complexity of Transformer self-attention mechanisms
- Benchmarks: Evaluated on Bangla fake news detection using Macro-F1 as the primary metric, comparing BanglaMamba against pre-trained BanglaBERT and a from-scratch CustomBERT with similar configuration
- Performance Results: BanglaBERT scored 0.9260 Macro-F1; BanglaMamba achieved 0.9029; CustomBERT (from scratch) achieved 0.9057
- Efficiency Gains: BanglaMamba achieves ~2.2× higher inference throughput and 49% lower peak GPU memory usage compared to BERT-based models
- Generalization: Cross-dataset evaluation shows BanglaBERT generalizes better to external datasets, highlighting the importance of large-scale pretraining for transferability
Industry Insight
- Organizations deploying NLP models for low-resource languages should evaluate Mamba-based architectures as a cost-effective alternative, especially when inference latency and GPU memory are bottlenecks
- The performance gap between pre-trained and from-scratch models reinforces the strategic value of investing in large-scale pretraining for domain-specific language tasks
- As misinformation detection scales globally, efficient architectures like BanglaMamba enable deployment in edge and resource-limited settings, broadening access to AI-powered content moderation tools
Disclaimer: The above content is generated by AI and is for reference only.