arXiv Machine Learning August 27, 2026 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 AI

Aug 25

Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings

arXiv:2507.12295v2 Announce Type: replace-cross Abstract: Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation id...

By Feng Xiao, Jicong Fan

llms rag nlp benchmarks

arXiv Machine Learning

Aug 6

BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning

arXiv:2608. 05104v1 Announce Type: new Abstract: Deep neural networks have shown impressive success in NLP tasks owing to their complex structure and huge number of edges.

By Sajib Hossain, Md Kamrus Samad, Anan Ghosh, Labib Imam Chowdhury, Nabeel Mohammed

llms nlp efficiency benchmarks

arXiv Computation and Language

Aug 25

HelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head

arXiv:2608.22922v1 Announce Type: new Abstract: We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourc...

By Thisen Ekanayake, Nisansa de Silva

llms nlp

arXiv AI

Aug 10

From Cheap Fakes to Pure Synthesis: Addressing the New Era of T2V Fake News Videos

arXiv:2608. 06732v1 Announce Type: new Abstract: Recent text-to-video (T2V) generation models enable fake news videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled from existing footage.

By Yifeng Luo, Yupeng Li, Liang Lan, Tian Wang

llms diffusion fine-tuning benchmarks safety

arXiv AI

Aug 25

Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku

arXiv:2608.22832v1 Announce Type: new Abstract: The social interactions among crowds via \textit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint confli...

By Xiansheng Luo, Chaowei Zhang, Zewei Zhang, Yi Zhu, Jipeng Qiang

multimodal benchmarks safety

arXiv Machine Learning

Jun 4

Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features

arXiv:2606. 04199v1 Announce Type: cross Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies.

By Aya Vera-Jimenez, Samuel Jaeger, Calvin Ibenye, Dhrubajyoti Ghosh

llms

One email a morning, machine-written

Related stories

Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings

BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning

HelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head

From Cheap Fakes to Pure Synthesis: Addressing the New Era of T2V Fake News Videos

Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku

Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features