Large language models (LLMs) have established state-of-the-art performance across many NLP tasks, yet their effectiveness for low-resource languages such as Urdu remains limited due to under-representation in pretraining corpora and inadequate tokenization. This study explores adapting pretrained LLMs to new languages by replacing the original tokenizer without cross-lingual correspondence. We show that stable adaptation depends on maintaining architectural compatibility, matching vocabulary size, and reusing pretrained embedding and language modeling head weights. We propose *Markhor*, a framework for adapting LLMs to low-resource languages in this way. It spans tokenizer training, continual pretraining, and instruction tuning via knowledge distillation, resulting in two models, *MKGPT* and *MKQwen*, based on GPT and Qwen architectures. These models are evaluated on multiple classification tasks (fake news detection, hate speech detection, emotion classification) and open-ended question answering. Results show both MKGPT and MKQwen outperform baselines across all tasks, suggesting the effectiveness of the methodology for developing open-source Urdu LLMs.

The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like ChatGPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood. Traditional fake news detection methods relying on linguistic cues have also become less effective. Moreover, current detectors primarily focus on binary classification and English texts, often overlooking the distinction between machine-generated true vs. fake news and the detection in low-resource languages. To this end, we updated the detection schema to include machine-generated news focusing on Urdu. We further propose a conjoint detection strategy to improve the accuracy and robustness. Experiments show its effectiveness across four datasets in various settings.