Large Language Models are increasingly deployed as writing assistants for usersin the Global South, yet rewriting prompts can suppress institutionalizedpostcolonial varieties. We quantify South Asian English (SAsE) dialect erasure ina state-of-the-art open-weight model using a 500-sentence diagnostic benchmark(320 lexical and 180 syntactic markers). On Llama 3.3 70B, standard grammarcorrection retains only 26.0% of markers (lexical 31.2%; syntactic 16.7%),while formalization is more destructive (14.0% overall retention). For lexicalitems, we observe Americanization in 56.2% (correction) and 59.4%(formalization) of cases, typically via Standard American paraphrases. A simpledialect-aware prompt raises retention to 92.0% and reduces lexicalAmericanization to 6.2%, although some function-word phenomena remain resistant. A stress test shows evenstronger suppression (6.7% retention). We position dialect erasure withinrepresentational-harm and cultural-competence frameworks, and provide areplicable protocol for auditing writing-assistance systems.

As reported, approximately 6 million people in Iraq and Iran speak in Sorani Kurdish, which exhibits substantial regional variation but lacks computational resources for dialect identification. We present the first fine-grained sub-dialect classification system for six Sorani varieties namely, Sulaymaniyah, Erbil, Iranian Sorani, Ardalani, Babani, and Mukriani. This investigation combines cross-lingual contextual embeddings (XLM-RoBERTa) with morphological features derived from explicit linguistic rules, including 24 patterns capturing verb prefixes, pronominal clitics, and definite markers. The suggested morphology-augmented XLM-R model has been trained on a unified dataset of 16,409 sentences without manual annotation, and achieves 91.91% accuracy, outperforming pure transformers (91.79%) and traditional machine learning baselines (SVM 86.41%). Key ablation studies reveal that morphological features serve as effective regularizers for geographically proximate dialects.

Hausa Ajami (Hausa written in Arabic script) remains severely under-resourced for computational morphology. We present AjamiMorph, a zero-annotation framework that discovers morphemes through consensus among three unsupervised methods, namely, Byte Pair Encoding (BPE), transition-based boundary detection using Pointwise Mutual Information (PMI), and computational linguistics based Distributional Affix Mining (DAM). Using a Hausa Ajami Bible corpus consisting of 637,414 tokens, AjamiMorph identifies 1,611 high-confidence morphemes, achieving 99.9% coverage. The inventory exhibits a linguistically realistic distribution (66.0% stems, 22.6% suffixes, 11.4% prefixes) and recovers 77.8% of known Hausa affixes. A permutation test that shuffles method assignments (preserving per-method selection sizes) confirms that the observed agreement is above-chance; chi-square remains as a secondary check. A lightweight 5-gram LM comparison (characters vs. consensus morphemes) provides an extrinsic signal. We also report negative results for script-driven Arabic assumptions and LLM-first annotation. This work provides the first unsupervised morpheme inventory for Hausa Ajami and demonstrates consensus as a robust strategy for zero-resource morphology.