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cs.CL2026
Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets
Sultan Alrashed, Francesco Orabona
Multilingual data from the web is essential for LLM pretraining. Yet, scraping it is expensive, and research groups repeatedly crawl the same content. For example, we found that ov…
cs.CL2025
SmolKalam: Ensemble Quality-Filtered Translation at Scale for High Quality Arabic Post-Training Data
Sultan Alrashed, Chadi Helwe, Francesco Orabona
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 ca…
cs.CL2025
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…