QwQ-32B

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1. Nov 2
   Epi2Diff predicts human item difficulty from LLM reasoning traces

   Researchers from Virginia Tech, the University of Maryland, MBZUAI and the University of Pittsburgh proposed Epi2Diff, a framework that splits LLM reasoning traces into cognitive episodes and combines them with item content to predict human difficulty. The research team reported first-place results across the main metrics for SAT Mathematics, SAT Reading and Writing, Cambridge and USMLE, with SAT Mathematics accuracy reaching 73.0%. Its paper was accepted by Transactions on Machine Learning Research.

   36氪 人工智能 · 🔥 0

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