Fake news detection in low-resource languages with LLMs

The proliferation of fake news is a global challenge to tackle in the digital era of information availability. The resourceful languages are tackling this issue through enormous research works whereas the low-resource languages are left behind to address the issue adequately. Bangla is one of the low-resource languages in computation despite being in the top ten most spoken languages in the world. To contribute in the field and address the issue of fake news, this research work focuses on the fake news detection in Bangla language leveraging large recent advancement of language models using cross-lingual prompting techniques for better response from the large language models. We leverage the open source models for resource accessibility and utilize DeepSeek-R1, Llama 3.2 and Qwen 2.5 large language models in our experiments and extensively analyze the fake news detection capacity of each model in Bangla language. We find that Qwen 2.5 outperforms the other models in this specific task achieving a maximum accuracy of 97.5 while it also reports no inconclusive response.

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