表面礼貌，实践错误：一个用于修复多语言孟加拉语生成中敬语失误的定制数据集
Polite on the Surface, Broken in Practice: A Curated Dataset for Fixing Generation and Register Failures in Low-Resource Bangla Text Generation
AI总结：
本文提出了一个定制数据集BLADE，用于改进多语言孟加拉语生成中敬语处理的准确性，通过系统微调和评估领先的开源架构，如DeepSeek-8B和LLaMA-3.2-3B，以提高结构忠实度和敬语对齐度。
摘要：
近年来，多语言大语言模型（MLLMs）的进展显著增强了跨语言对话能力，但建模文化细腻和上下文依赖的交流仍是一个关键瓶颈。具体而言，现有最先进的模型在处理低资源环境如孟加拉语中的结构变化、地区习语和敬语一致性时存在严重的语用差距。为了解决这一限制，我们引入了一个新的、文化对齐的指令微调数据集和基准框架，即BangLa Application and DialoguE生成-BLADE，包含4,196对精心编纂的交互对。我们利用这一资源系统地微调和评估领先的开源架构，包括DeepSeek-8B和LLaMA-3.2-3B，通过LoRA适配器在4位NormalFloat（NF4）量化框架下进行参数高效微调。我们的实证评估表明，使用我们数据集微调的模型在结构忠实度和敬语对齐方面有显著改进，为弥合低资源多语言文本生成中的语用差距提供了严格基准。代码和数据集：https://github.com/ashuvo25/Bangla_Application_LLM/tree/main
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
