arXiv Machine Learning By M. K. Khalidi Siam

BanglaMamba: Exploring State Space Models for Bangla Fake News Detection

Read the original on arXiv Machine Learning →

BanglaMamba explores Mamba-based State Space Models (SSMs) as a computationally efficient alternative for Bangla fake news detection. Compared to BanglaBERT and a custom BERT trained from scratch, BanglaMamba achieves a Macro‑F1 score of 0.9029, close to the 0.9057 of the custom BERT, while delivering 2.2× higher inference throughput and 49% lower peak GPU memory usage. Cross‑dataset evaluation shows BanglaBERT generalizes better, underscoring the value of large‑scale pretraining.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Computation and Language
Oct 12

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

SharP is a text‑generation framework that uses both hard and soft prompts to guide large language models in creating task‑specific synthetic news samples for low‑resource fake news detection. The method combines structured factual constraints with learned semantic patterns, and employs a boundary‑aware strategy that focuses on classifier‑uncertain areas to refine decision boundaries. Dual objective optimization balances semantic alignment with source data and classifier feedback, aiming to improve robustness and generalization when labeled data are scarce.

By Ziyu Luo, Wei Li, Haoping Huang, Kecheng Liu, Min Gao
arXiv AI
Oct 5

A Unified BERT-CNN-BiLSTM Framework for Simultaneous Headline Classification and Sentiment Analysis of Bangla News

The paper introduces a hybrid BERT‑CNN‑BiLSTM model for simultaneously classifying Bangla news headlines and performing sentiment analysis. Using the BAN‑ABSA dataset of 9,014 headlines, the authors compare two sampling strategies and report headline accuracy up to 81.37 % and sentiment accuracy up to 73.43 %. The proposed framework outperforms baseline models and establishes new state‑of‑the‑art results for Bangla headline classification and sentiment analysis.

By Mirza Raquib, Munazer Montasir Akash, Tawhid Ahmed, Saydul Akbar Murad, Farida Siddiqi Prity, Mohammad Amzad Hossain, Asif Pervez Polok, Nick Rahimi