M-DaQ：用于指令微调数据集的多语言多样性与质量样本检索
M-DaQ: Retrieving Samples with Multilingual Diversity and Quality for Instruction Fine-Tuning Datasets
AI总结：
M-DaQ通过联合优化指令-响应质量与跨语言语义多样性，构建高质量平衡训练数据，验证了多语言设置下的Superficial Alignment Hypothesis，并在18种语言上展示出超过60%的胜率。
摘要：
多语言指令微调（IFT）使大语言模型能够跨多样语言和文化背景进行泛化；然而，高质量、系统性整理的多语言IFT数据集仍然稀缺。为解决这一缺口，我们提出了M-DaQ（多语言多样性与质量），一种多样性意识的采样框架，同时优化指令-响应质量与跨语言语义多样性。M-DaQ利用微调后的质量评分模型和受最大边际相关性启发的选取策略来构建平衡、高保真的训练数据。此外，我们首次系统地探讨了多语言环境下的Superficial Alignment Hypothesis。在18种语言上的广泛评估显示，训练于M-DaQ整理数据的模型在Alpaca-Eval和MT-Bench上平均胜率超过60%。补充的人类评估证实了这些收益，突显了文化相关性、语境适当性和指令遵循能力的显著提升。代码已公开发布，以促进可重复性和未来研究。
英文摘要：
Multilingual instruction fine-tuning (IFT) empowers large language models to generalize across diverse linguistic and cultural contexts; however, high-quality, systematically curated multilingual IFT datasets remain scarce. To address this gap, we propose M-DaQ (Multilingual Diversity and Quality), a diversity-aware sampling framework that jointly optimizes instruction-response quality and cross-lingual semantic diversity. M-DaQ leverages a fine-tuned Quality Scoring Model alongside a maximal marginal relevance-inspired selection strategy to construct balanced, high-fidelity training data. Furthermore, we present the first systematic investigation of the Superficial Alignment Hypothesis in multilingual settings. Extensive evaluations across 18 languages demonstrate that models trained on M-DaQ-curated data achieve average win rates exceeding 60% against strong baselines on Alpaca-Eval and MT-Bench. Complementary human evaluations corroborate these gains, highlighting significant improvements in cultural relevance, contextual appropriateness, and instruction-following capability. The code are publicly released to facilitate reproducibility and future research.
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。
