Edu-QuRating: Multi-Dimensional Educational Data Curation with Distilled Pairwise Judgements

Researchers have introduced Edu-QuRating, a data-filtering method for language-model pre-training that evaluates educational value across multiple dimensions instead of a single scalar score. The approach relies on distilled pairwise judgements to rank documents, giving finer-grained control when curating training corpora. The work argues that one-dimensional educational quality metrics can be too coarse for datasets with specific application needs.