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-      High-quality multilingual data is crucial for training effective large language models (LLMs).<br>
-      <strong>JQL (Judging Quality across Languages)</strong> is a scalable and lightweight data filtering approach that distills the judgment capabilities of strong multilingual LLMs into efficient cross-lingual annotators. These annotators enable robust filtering of web-scale data.
+      High-quality multilingual data is crucial for training effective large language models (LLMs).
+      <strong>JQL (Judging Quality across Languages)</strong> is a scalable and lightweight multilingual data filtering approach that distills the judgment capabilities of strong 
+      multilingual LLMs into efficient cross-lingual annotators. 
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-      JQL improves data quality, retains more tokens, and generalizes beyond high-resource European languages—achieving strong performance on Arabic, Thai, and Mandarin. It outperforms heuristic baselines and enables efficient multilingual pretraining data curation at scale.
+      Overall, JQL improves data quality, retains more tokens, and generalizes to unseen languages. It outperforms heuristic baselines and enables cost-efficient multilingual pretraining data curation at scale.
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