Improving Fake News Detection in Low-Resource Languages through Retrieval-Augmented Generation And Parameter-Efficient Fine-Tuning

A S M Humaun Kabir, Sameed Ahmed Khan, Alexander Kharlamov, Ilia Voronkov

Abstract


The proliferation of fake news in low-resource languages poses critical societal challenges due to limited resources, insufficient NLP tooling, and the morphological complexity of non-English scripts. Bangla, the seventh most spoken language in the world, remains severely under-resourced in automated fake news detection research. This paper presents a systematic comparative evaluation of three adaptation strategies for Bangla fake news detection using the Qwen3.5-4B multilingual large language model: vanilla zero-shot settings, Retrieval-Augmented Generation (RAG) backed by vector database with multilingual sentence embeddings, and parameter-efficient fine-tuning via Low-Rank Adaptation (LoRA). Experiments on a balanced dataset of 2,078 Bangla news articles demonstrate that RAG improves accuracy by 6.73 percentage points over the zero-shot baseline from 73.85% to 80.58%, while LoRA fine-tuning with correct inference-time evaluation methodology achieves near-perfect classification at 99.04% accuracy and macro F1 of 0.9904. This work establishes strong baselines for Bangla fake news detection and contributes methodological insights critical for deploying fine-tuned LLMs in low-resource settings.

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References


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