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		<title>Adaptive Temperature Distillation Shrinks Multilingual AI Models for Classroom Use</title>
		<link>https://bioengineer.org/adaptive-temperature-distillation-shrinks-multilingual-ai-models-for-classroom-use/</link>
		
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		<pubDate>Sun, 25 Oct 2026 03:23:13 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[adaptive temperature]]></category>
		<category><![CDATA[cross-lingual transfer]]></category>
		<category><![CDATA[curriculum learning]]></category>
		<category><![CDATA[educational AI]]></category>
		<category><![CDATA[knowledge distillation]]></category>
		<category><![CDATA[low-resource languages]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[model compression]]></category>
		<category><![CDATA[multilingual language models]]></category>
		<category><![CDATA[Natural Language Processing]]></category>
		<category><![CDATA[Neural Networks]]></category>
		<category><![CDATA[XLM-RoBERTa]]></category>
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					<description><![CDATA[A new distillation framework that adjusts its temperature per language and sample compresses a 560-million-parameter multilingual model into a 66-million-parameter student that retains 97.8 percent of the teacher's accuracy while running 8.5 times faster.]]></description>
		
		
		
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