propella-1: Multi-Property Document Annotation for LLM Data Curation at Scale

• URL: http://arxiv.org/abs/2602.12414v2
• Date: Thu, 19 Feb 2026 09:31:25 GMT
• Title: propella-1: Multi-Property Document Annotation for LLM Data Curation at Scale
• Abstract summary: A single score conflates multiple quality dimensions, prevents flexible filtering, and offers no interpretability.<n>We introduce propella-1, a family of small multilingual LLMs that annotate text documents across 18 properties organized into six categories.<n>We release a dataset of over three billion document annotations covering major pretraining corpora including data from FineWeb-2, FinePDFs, HPLT 3.0, and Nemotron-CC.
• Score: 0.10888485668490162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 as a reference annotator, the 4B model achieves higher agreement than much larger general-purpose models. We release propella-annotations, a dataset of over three billion document annotations covering major pretraining corpora including data from FineWeb-2, FinePDFs, HPLT 3.0, and Nemotron-CC. Using these annotations, we present a multi-dimensional compositional analysis of widely used pretraining datasets, revealing substantial differences in quality, reasoning depth, and content composition that single-score approaches cannot capture. All model weights and annotations are released under permissive, commercial-use licenses.

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