SmolKalam: Ensemble Quality-Filtered Translation at Scale for High Quality Arabic Post-Training Data
Year: 2025
Type: preprint
Abstract: Although the community has tackled the acquisition of high-quality Arabic pretraining data, we still lack large-scale, multi-turn Arabic datasets that include reasoning and tool calling. Naive translation can work at the pretraining scale, but post-training demands much higher quality, which requires a stricter approach to dataset curation. In this work, we introduce SmolKalam, a translation of Smoltalk2 that uses a multi-model ensemble translation pipeline, applies quality filtering, and examines effective translation techniques for traditional decoder-only models through ablations.
Source: arXiv (Cornell University)
Authors: Alrashed, Sultan, Helwe, Chadi, Orabona, Francesco
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7106682941
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