Low-resource languages like Urdu suffer from limited high quality parallel data for machine translation. We introduce a curated English–Urdu corpus of 80,749 high-fidelity sentence pairs across 18 diverse domains, built via ethical collection, manual alignment, deduplication, and strict length-based filtering (AWCD ≤ 5). The corpus is converted into a bidirectional SFT dataset with bilingual (English/Urdu) instructions to enhance prompt-language robustness. Fine-tuning Llama-3.1-8B-Instruct (Llama-FT) and UrduLlama 1.1 (UrduLlama-FT) yields major gains over the baseline. sacreBLEU scores reach 24.65–25.24 (En→Ur) and 76.14–77.97 (Ur→En) for Llama-FT, with minimal sensitivity to prompt language. Blind human evaluation on 90 sentences per direction confirms substantial perceptual improvements. Results demonstrate the value of clean parallel data and bilingual instruction tuning, revealing complementary benefits of general SFT versus Urdu specific pretraining. This work provides a reproducible resource and pipeline to advance Urdu machine translation and similar low-resource languages.

In this paper, we provide a brief summary of the inaugural workshop on Challenges in Processing South Asian Languages (CHiPSAL) held as part of COLING 2025. The workshop included regular papers, invited keynotes, and shared task papers, fostering a collaborative platform for exploring challenges in processing South Asian languages. The shared task focused on Devanagari-script language understanding, encompassing subtasks on language identification, hate speech detection, and target classification. This workshop series aims to address linguistic and cultural nuances, resource constraints, and orthographic complexities in low-resource South Asian languages while advancing NLP research and promoting multilingual inclusivity.

Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majority of existing multilingual NLP benchmarks for LLMs provide evaluation data in only few languages with little linguistic diversity. In addition these benchmarks lack quality assessment against the respective state-of the art models. This study presents an in-depth examination of 7 prominent LLMs: GPT-3.5-turbo, Llama 2-7B-Chat, Llama 3.1-8B, Bloomz 3B, Bloomz 7B1, Ministral-8B and Whisper (Large, medium and small variant) across 17 tasks using 22 datasets, 13.8 hours of speech, in a zero-shot setting, and their performance against state-of-the-art (SOTA) models, has been compared and analyzed. Our experiments show that SOTA models currently outperform encoder-decoder models in majority of Urdu NLP tasks under zero-shot settings. However, comparing Llama 3.1-8B over prior version Llama 2-7B-Chat, we can deduce that with improved language coverage, LLMs can surpass these SOTA models. Our results emphasize that models with fewer parameters but richer language-specific data, like Llama 3.1-8B, often outperform larger models with lower language diversity, such as GPT-3.5, in several tasks.