做低资源语言NLP或文化敏感对话系统的团队，这个数据集直接解决了敬语对齐的痛点，可以拿来微调自己的模型试试。
多语言大模型在处理孟加拉语等低资源语言时，常因文化语境缺失导致敬语使用错误。研究者构建了BLADE数据集，包含4196个精心设计的对话对，用于指令微调。通过LoRA适配器对DeepSeek-8B和LLaMA-3.2-3B进行参数高效微调，模型在结构保真度和敬语对齐上显著提升。该工作为低资源多语言生成中的语用鸿沟提供了基准。代码和数据集已开源。
Polite on the Surface, Wrong in Practice: A Curated Dataset for Fixing Honorific Failures in Multilingual Bangla Generation
Recent advances in Multilingual Large Language Models (MLLMs) have significantly enhanced cross-lingual conversational capabilities, yet modeling culturally nuanced and context-dependent communication remains a critical bottleneck. Specifically, existing state-of-the-art models exhibit a severe pragmatic gap when handling structural variations, regional idioms, and honorific consistencies in low-resource contexts like Bangla. To address this limitation, we introduce a novel, culturally aligned instruction-tuning dataset for \textbf{BangLa Application and DialoguE generation - BLADE} and benchmarking framework comprising $4,196$ meticulously curated interaction pairs. We leverage this resource to systematically fine-tune and evaluate leading open-weight architectures, including DeepSeek-8B and LLaMA-3.2-3B, utilizing parameter-efficient fine-tuning via LoRA adapters in a 4-bit NormalFloat (NF4) quantization framework. Our empirical evaluations demonstrate that models fine-tuned on our dataset yield substantial improvements in structural fidelity and honorific alignment, providing a rigorous benchmark for bridging pragmatic disparities in low-resource multilingual text generation. Code and dataset: https://github.com/ashuvo25/Bangla_Application_LLM/tree/main