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Fondazione Bruno Kessler, Trento, Italy
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Unlabeled data can be transformed into powerful training examples, boosting classification performance by nearly 20 points without the need for costly annotations.
Small language models can not only match but significantly outperform their larger counterparts in specialized medical NLP tasks, challenging the assumption that bigger is always better.
