Lius：基于持续指令调优的库邦马来语教学语言学翻译模型
Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay
AI总结：
针对低资源语言库邦马来语，提出利用双语词典的词汇和语义特征设计指令，并采用持续指令调优（CIT）范式微调大语言模型，在多个指标上超越基线4-6分，优于NMT和多语言LLM 10-13分。
摘要：
大语言模型（LLM）为翻译任务提供了新的潜力，但在处理低资源语言时常常出现性能下降。为了解决这一限制，我们提出了一种针对低资源语言库邦马来语微调LLM的方法。我们的方法涉及利用双语词典的显式词汇和语义特征设计一组指令，并引入持续指令调优（CIT），一种支持基于迭代指令训练的训练范式。实验结果表明，我们名为Lius的模型在多个评估指标上比标准指令调优模型提高了4-6分，并超越了神经机器翻译（NMT）和多语言LLM模型10-13分。这些发现突显了我们的方法在减轻低资源语言翻译中对大规模并行数据依赖的潜力。
英文摘要：
Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages. To address this limitation, we propose an approach for fine-tuning LLMs on a low-resource language, Kupang Malay. Our approach involves designing a set of instructions by leveraging explicit lexical and semantic features from a bilingual dictionary, and introducing Continual Instruction Tuning (CIT), a training paradigm that enables iterative instruction-based training. Experimental results demonstrate that our model, named Lius, yields notable improvements over standard instruction-tuned models by outperforming 4-6 points, and surpassing both Neural Machine Translation (NMT) and Multilingual LLM models by 10-13 points on several evaluation metrics. These findings highlight the potential of our approach to mitigate the reliance on large-scale parallel data in low-resource language translation.
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