LuxIT：从单语种子数据生成的卢森堡语指令微调数据集
LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data
AI总结：
本文提出LuxIT数据集，通过合成高质量卢森堡语指令-回答对，提升低资源语言LLM性能，实验显示在语言考试和NLP任务中均取得显著提升。
摘要：
指令微调大型语言模型（LLM）在低资源语言环境中效果受限，主要由于缺乏高质量训练数据。我们介绍了LuxIT，一个全新的单语指令微调数据集，用于缓解这一挑战。我们从原生卢森堡语文本语料库中合成该数据集，使用DeepSeek-R1-0528，因其在卢森堡语中表现出色。生成后，我们采用LLM作为判断者的方法进行质量保障，保留了227,507对高质量的指令-回答对。为了研究该数据集的实用性，我们对14个较小规模的LLM（≤15B参数）在LuxIT上进行微调，并在标准化的卢森堡语能力考试和五个下游NLP任务上进行评估。在语言考试上，所有14个模型的平均准确率提高了5.37个百分点，其中12个模型有所提升。在NLP下游任务中，9个模型在宏平均F1上有所改进，尽管两个基准上的增益并不系统相关。这些结果强调了利用单语合成数据提升低资源语言LLM能力的可行性，同时突显了语言能力的多面性。
英文摘要：
The effectiveness of instruction-tuned Large Language Models (LLMs) is often limited in low-resource linguistic settings due to a lack of high-quality training data. We introduce LuxIT, a monolingual instruction tuning dataset for Luxembourgish developed to mitigate this challenge. We synthesize the dataset from a corpus of native Luxembourgish texts, utilizing DeepSeek-R1-0528, chosen for its shown proficiency in Luxembourgish. Following generation, we apply a quality assurance process, employing an LLM-as-a-judge approach, retaining 227,507 high-quality instruction-answer pairs. To investigate the practical utility of the dataset, we fine-tune 14 smaller-scale LLMs ($\leq$15B parameters) on LuxIT and evaluate them on standardized Luxembourgish proficiency exams and five downstream NLP tasks. Training on LuxIT yields a mean accuracy change of +5.37 percentage points on language exams across all 14 models, with 12 of 14 showing improvement. On NLP downstream tasks, 9 of 14 models improve in macro-averaged F1, though gains on the two benchmarks do not systematically correlate. These results underscore the feasibility of leveraging monolingual synthetic data to improve LLM capabilities in low-resource languages, while highlighting the multi-faceted nature of language proficiency.
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。
