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Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification

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arXiv:2606. 18372v1 Announce Type: new Abstract: Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture personally identifiable information (PII) entangled with curricular content, where "Riemann" may refer to a real student or to a mathematical concept.

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Authors: Haocheng Zhang, Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham, Ren\'e F. Kizilcec
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, text, persona, eval, source, line, rate. Source: arxiv cs.CL (NLP).
arXiv: 2606.18372

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