propella-1: Multi-Property Document Annotation for LLM Data Curation at Scale
Since FineWeb-Edu, data curation for LLM pretraining has predominantly relied on single scalar quality scores produced by small classifiers. A single score conflates multiple quality dimensions, prevents flexible filtering, and offers no interpretability. We introduce propella-1, a family of small multilingual LLMs (0.6B, 1.7B, 4B parameters) that annotate text documents across 18 properties organized into six categories: core content, classification, quality and value, audience and purpose, safety and compliance, and geographic relevance. The models support 57 languages and produce structured JSON annotations conforming to a predefined schema. Evaluated against a frontier commercial LLM ...
| Authors | Maximilian Idahl, Benedikt Droste, Björn Plüster et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 February 2026 |
| Last updated at source | 19 February 2026 |
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