Redwood uses distillation to audit models; confession hits 84%
Redwood Research's new paper distills untrusted models into same-base students to make them confess hidden traits, lifting one trait's confession rate from 22% to 84%.
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Redwood Research's new paper distills untrusted models into same-base students to make them confess hidden traits, lifting one trait's confession rate from 22% to 84%.
The preprint server says papers with obvious unreviewed LLM output, hallucinated citations or leftover model comments can trigger a year-long submission ban.