马斯特里赫特大学AMIYA：通过微调和MBR解码适应方言阿拉伯语
Maastricht University at AMIYA: Adapting LLMs for Dialectal Arabic using Fine-tuning and MBR Decoding
AI总结：
本文通过LoRA微调和MBR解码技术，提升方言阿拉伯语的生成与翻译性能，实验显示该方法在保持语义准确性的基础上提高了方言忠实度。
摘要：
大型语言模型（LLMs）正变得越来越多语言，支持数百种语言，尤其是资源丰富的语言。不幸的是，方言变体仍然由于数据有限和语言变异而代表性不足。在本工作中，我们适应了一个预训练的LLM以提高方言性能。具体而言，我们使用低秩适应（LoRA）在单语和英语方言平行数据上进行微调，适配器合并和方言感知的MBR解码，以提高方言忠实生成和翻译。在叙利亚、摩洛哥和沙特阿拉伯的实验中，合并和MBR提高了方言忠实性的同时保持了语义准确性。这种结合提供了一个紧凑而有效的框架，用于鲁棒的方言阿拉伯语生成。
英文摘要：
Large Language Models (LLMs) are becoming increasingly multilingual, supporting hundreds of languages, especially high resource ones. Unfortunately, Dialect variations are still underrepresented due to limited data and linguistic variation. In this work, we adapt a pre-trained LLM to improve dialectal performance. Specifically, we use Low Rank Adaptation (LoRA) fine-tuning on monolingual and English Dialect parallel data, adapter merging and dialect-aware MBR decoding to improve dialectal fidelity generation and translation. Experiments on Syrian, Moroccan, and Saudi Arabic show that merging and MBR improve dialectal fidelity while preserving semantic accuracy. This combination provides a compact and effective framework for robust dialectal Arabic generation.
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。
