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Headlines · 2
- Protegrity releases 278M-parameter open-weights PII detector
Protegrity announced a 278M-parameter, open-weights PII detection model, a supporting script, the PII-SCOPE benchmark and a research paper. The developer says the RoBERTa fine-tune was trained on 1.2B synthetic text samples containing varied PII and can identify PII instances by context, returning each instance's index and type or redacting all detected PII. PII-SCOPE was designed to address common issues in public LLM-generated datasets and is available on Hugging Face.
r/MachineLearning · 🔥 0 - 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.
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