MIDB：用于提升多语言指令合成中文化平等性的多语言指令数据增强器
MIDB: Multilingual Instruction Data Booster for Enhancing Cultural Equality in Multilingual Instruction Synthesis
AI总结：
针对多语言指令合成中因机器翻译缺陷、本地化不足导致的文化不平等问题，提出基于16种语言约36.8k条专家修订示例训练的MIDB，提升数据质量并增强多语言LLMs的指令遵循与文化理解能力。
摘要：
尽管对数据质量存在质疑，指令合成仍作为一种经济快速的替代方案被广泛应用于大型语言模型（LLMs）的指令调优（IT）。近期研究聚焦于提升英文合成指令对的数据质量，并推动了以英文为中心的LLMs的指令调优。然而，多语言合成指令对的数据质量问题更为严重，因为常见的合成做法是使用机器翻译（MT）将英文合成数据翻译成其他语言。除了这些英文合成数据中已知的内容错误外，多语言合成指令数据还进一步暴露于机器翻译引入的缺陷，且面临目标语言本地化不足的问题，导致训练后的LLMs存在文化不平等。本文提出MIDB——一种多语言指令数据增强器，用于自动解决多语言合成数据中的质量问题。MIDB基于人类语言专家提供的16种语言的约36.8k条修订示例进行训练，能够通过解决内容错误、机器翻译缺陷及提升这些合成数据的本地化水平来增强低质量数据。自动和人工评估均表明，MIDB不仅稳定提升了16种语言的指令数据质量，还显著增强了在MIDB增强数据上微调的多语言LLMs的指令遵循能力和文化理解能力，表明其提升了语言和文化平等性。
英文摘要：
Despite doubts on data quality, instruction synthesis has been widely applied into instruction tuning (IT) of LLMs as an economic and rapid alternative. Recent endeavors focus on improving data quality for synthesized instruction pairs in English and have facilitated IT of English-centric LLMs. However, data quality issues in multilingual synthesized instruction pairs are even more severe, since the common synthesizing practice is to translate English synthesized data into other languages using machine translation (MT). Besides the known content errors in these English synthesized data, multilingual synthesized instruction data are further exposed to defects introduced by MT and face insufficient localization of the target languages, leading to cultural inequality in trained LLMs. In this paper, we propose MIDB, a Multilingual Instruction Data Booster to automatically address the quality issues in multilingual synthesized data. MIDB is trained on around 36.8k revision examples across 16 languages by human linguistic experts, thereby can boost the low-quality data by addressing content errors and MT defects, and improving localization in these synthesized data. Both automatic and human evaluation indicate that not only MIDB steadily improved instruction data quality in 16 languages, but also the instruction-following and cultural-understanding abilities of multilingual LLMs fine-tuned on MIDB-boosted data were significantly enhanced, suggesting an improved linguistic and cultural equality.
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。
