# Unified Large Language Models for Misinformation Detection in Low-Resource Linguistic Settings

arXiv: https://arxiv.org/abs/2506.01587 · HTML: https://mlsanity.com/paper/2506.01587/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages](https://mlsanity.com/paper/2204.09653/) (2022)
- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [UQA: Corpus for Urdu Question Answering](https://mlsanity.com/paper/2405.01458/) (2024)
- [Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification](https://mlsanity.com/paper/2003.01345/) (2020)
- [Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection](https://mlsanity.com/paper/2403.14037/) (2024)

## Built on by (most cited first, 1 of 1)

- [DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text](https://mlsanity.com/paper/2608.01046/) (2026)

## Abstract (arXiv)

The rapid expansion of social media platforms has significantly increased the dissemination of forged content and misinformation, making the detection of fake news a critical area of research. Although fact-checking efforts predominantly focus on English-language news, there is a noticeable gap in resources and strategies to detect news in regional languages, such as Urdu. Advanced Fake News Detection (FND) techniques rely heavily on large, accurately labeled datasets. However, FND in under-resourced languages like Urdu faces substantial challenges due to the scarcity of extensive corpora and the lack of validated lexical resources. Current Urdu fake news datasets are often domain-specific and inaccessible to the public. They also lack human verification, relying mainly on unverified English-to-Urdu translations, which compromises their reliability in practical applications. This study highlights the necessity of developing reliable, expert-verified, and domain-independent Urdu-enhanced FND datasets to improve fake news detection in Urdu and other resource-constrained languages. This paper presents the first benchmark large FND dataset for Urdu news, which is publicly available for validation and deep analysis. We also evaluate this dataset using multiple state-of-the-art pre-trained large language models (LLMs), such as XLNet, mBERT, XLM-RoBERTa, RoBERTa, DistilBERT, and DeBERTa. Additionally, we propose a unified LLM model that outperforms the others with different embedding and feature extraction techniques. The performance of these models is compared based on accuracy, F1 score, precision, recall, and human judgment for vetting the sample results of news.