X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
Chong Li, Wen Tao Yang, Jiajun Zhang, Jinliang Lu, Shaonan Wang, Chengqing Zong · 2024
Large language models respond well in highresource languages like English but struggle in low-resource languages.It may arise from the lack of high-quality instruction following data in these languages.Directly translating English samples into these languages can be a solution but unreliable, leading to responses with translation errors and lacking languagespecific or cultural knowledge.To address this issue, we propose a novel method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages.Specifically, the language model first learns to generate appropriate English instructions according to the natural web texts in other languages as responses.The candidate cross-lingual instruction tuning samples are further refined and diversified.We have employed this method to build a large-scale cross-lingual instruction tuning dataset on 10 languages, namely X-Instruction.The instruction data built using our method incorporate more language-specific knowledge compared with the naive translation method.Experimental results have shown that the response quality of the model tuned on X-Instruction greatly exceeds the model distilled from a powerful teacher model, reaching or even surpassing the ones of ChatGPT.In addition, we find that models tuned on cross-lingual instruction following samples can follow the instruction in the output language without further tuning.1 † These authors contributed equally to this work.