Edu-QuRating: Multi-Dimensional Educational Data Curation with Distilled Pairwise Judgements
Educational data filters have become a practical way to improve language-model pre-training, but most filters treat educational value as a single scalar property. This may be too broad for some applications, especially if the data set already features a high density of educational material. Useful learning material needs to be accurate, engaging, well structured, and appropriate for the intended audience and application (e.g. learner- vs teacher-facing). Following QuRating (Wettig et al. 2024), we introduce Edu-QuRating: a pipeline for multi-dimensional educational data scoring and curation. Edu-QuRating defines education-specific rubrics, uses an LLM judge to label sampled document pairs...
| Authors | Oliver G. B. Garrod, Robin A. A. Ince, Meng Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 September 2026 |
| Last updated at source | 10 September 2026 |
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