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X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions

Annual Meeting of the Association for Computational Linguistics(2024)

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Abstract
Large language models respond well in high-resource languages like Englishbut struggle in low-resource languages. It may arise from the lack ofhigh-quality instruction following data in these languages. Directlytranslating English samples into these languages can be a solution butunreliable, leading to responses with translation errors and lackinglanguage-specific or cultural knowledge. To address this issue, we propose anovel method to construct cross-lingual instruction following samples withinstruction in English and response in low-resource languages. Specifically,the language model first learns to generate appropriate English instructionsaccording to the natural web texts in other languages as responses. Thecandidate cross-lingual instruction tuning samples are further refined anddiversified. We have employed this method to build a large-scale cross-lingualinstruction tuning dataset on 10 languages, namely X-Instruction. Theinstruction data built using our method incorporate more language-specificknowledge compared with the naive translation method. Experimental results haveshown that the response quality of the model tuned on X-Instruction greatlyexceeds the model distilled from a powerful teacher model, reaching or evensurpassing the ones of ChatGPT. In addition, we find that models tuned oncross-lingual instruction following samples can follow the instruction in theoutput language without further tuning.
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要点】:论文提出了一种新颖的方法,通过自制的跨语言指令构建低资源语言的语言模型,以提高其响应质量和语言文化适应性。

方法】:该方法首先让语言模型学习根据其他语言的网页文本生成合适的英语指令,然后进一步优化和多样化候选的跨语言指令调整样本。

实验】:作者使用该方法构建了一个包含10种语言的跨语言指令调整数据集X-Instruction,实验结果表明,基于X-Instruction调整的模型响应质量远超从强教师模型提炼的模型,达到甚至超过ChatGPT的水平。此外,调整后的模型可以在不进行额外调整的情况下遵循输出语言的指令。