MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining
This addresses data quality selection for multilingual LLM pretraining, which is an incremental improvement over existing English-focused methods.
The authors tackled the problem of multilingual data selection for large language model pretraining by developing MuRating, a framework that transfers English data-quality signals to 17 target languages. Their approach improved average accuracy on English and multilingual benchmarks, with particularly large gains on knowledge-intensive tasks.
Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English. We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a single rater for 17 target languages. MuRating aggregates multiple English "raters" via pairwise comparisons to learn unified document-quality scores,then projects these judgments through translation to train a multilingual evaluator on monolingual, cross-lingual, and parallel text pairs. Applied to web data, MuRating selects balanced subsets of English and multilingual content to pretrain a 1.2 B-parameter LLaMA model. Compared to strong baselines, including QuRater, AskLLM, DCLM and so on, our approach boosts average accuracy on both English benchmarks and multilingual evaluations, with especially large gains on knowledge-intensive tasks. We further analyze translation fidelity, selection biases, and underrepresentation of narrative material, outlining directions for future work.