A massive participatory commonsense-reasoning benchmark built by 350+ researchers from 65+ countries, covering 141 language varieties across five continents, 19 language families, and 24 writing systems. I verified translations and integrated culturally specific elements for the Balochi language, helping assess cross-lingual performance gaps — up to 68% accuracy difference between languages — in state-of-the-art LLMs.

Proposes a sub-word tokenization scheme tailored to Balochi's morphology and Perso-Arabic script, reducing out-of-vocabulary rates and improving downstream NLP performance compared to standard tokenizers trained on high-resource languages.

Develops a language identification model that distinguishes Balochi from closely related Perso-Arabic-script languages, supporting reliable corpus collection and filtering for low-resource NLP pipelines.

Introduces BNer, the first gold-standard Named Entity Recognition dataset for the Balochi language — 58,893 tokens across five domains. Evaluates CRFs, SVMs, and KNNs; SVM achieves an optimal 96% F1-score by leveraging POS features and the BILOU tagging scheme.

A comprehensive systematic review of 100+ research papers (2013–2025) tracking the evolution of NLP techniques — from static word embeddings to large language models — across Perso-Arabic-script languages. Identifies critical resource gaps for understudied languages like Balochi and outlines future directions for a unified NLP ecosystem.