MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining

TL;DR

MuRating is a scalable framework that transfers English data quality signals to multiple languages, improving multilingual large language model pretraining by selecting high-quality data across 17 languages.

Contribution

Introduces MuRating, a novel multilingual data selection method that leverages English quality signals and translation to enhance LLM pretraining across diverse languages.

Findings

Boosts accuracy on English benchmarks and multilingual evaluations.

Achieves significant gains on knowledge-intensive tasks.

Effectively balances multilingual data selection for pretraining.

Abstract

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.