M-DaQ introduces a diversity-aware sampling framework combining a quality scoring model with maximal marginal relevance selection to build multilingual instruction fine-tuning datasets, yielding models with over 60% average win rates on Alpaca-Eval and MT-Bench across 18 languages.
This method jointly optimizes both sample quality (via QSM) and diversity (via un- supervised clustering algorithm) for Multilingual IFT dataset
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M-DaQ: Retrieving Samples with Multilingual Diversity and Quality for Instruction Fine-Tuning Datasets
M-DaQ introduces a diversity-aware sampling framework combining a quality scoring model with maximal marginal relevance selection to build multilingual instruction fine-tuning datasets, yielding models with over 60% average win rates on Alpaca-Eval and MT-Bench across 18 languages.