Many Bangla words are at once personal names and culturally loaded common nouns: মায়া (Maya) is both a girl’s name and a word for affectionate compassion. Choosing the right reading demands cultural knowledge that is scarce in the pretraining data of modern language models. We introduce Bangla Culturally Entangled Homograph (BanglaCEH) disambiguation and build a Bangla benchmark of 1,516 expert-verified sentences and 3,032 labelled occurrences in which one word appears twice with two distinct readings, each labelled with a culturally grounded category and an explanation of the reasoning behind it. Across open- and closed-source models, we find a systematic dominant-meaning bias, models default to the common-noun sense and overlook the name. A Bangla-specific model fails under every prompting regime we test, showing that language-specific pretraining alone does not confer cultural grounding. We further show that contrastive chain-of-thought prompting can sharply reduce this bias without training, and that distilling cultural explanations teaches small (1–3B) models to reason toward the correct reading rather than memorise labels, cutting dominant-meaning bias from as high as 100% to under 5% and turning the failed Bangla-specific model into our strongest system.

Recent advances in Multilingual Large Language Models (MLLMs) have improved cross-lingual conversational capabilities, yet generating reliable, institutionally usable text in low-resource languages like Bangla remains a critical bottleneck, particularly for compact, low-resource LLMs. Beyond surface fluency, state-of-the-art and small open-weight models frequently exhibit deeper generation failures in Bangla: incoherent or off-topic output, structural misformatting, and inconsistent honorific registers, that render text unusable in formal contexts. To address this, we introduce BLADE (BangLa Application and DialoguE generation), a culturally aligned instruction-tuning dataset and benchmarking framework comprising 4,196 curated interaction pairs. We use this resource to fine-tune and evaluate compact open-weight architectures, including DeepSeek-8B, LLaMA-3.2-3B, Qwen2, and TigerLLM-1B, via LoRA adapters under 4-bit NormalFloat (NF4) quantization on limited hardware. Our evaluations show that targeted fine-tuning on BLADE yields substantial improvements in generation reliability, structural fidelity, and register consistency even for the smallest models, establishing a rigorous benchmark for closing generation and pragmatic gaps in low-resource multilingual text generation. Code and dataset: https://github.com/ashuvo25/Bangla_Application_LLM