EXPLORING CLAUDE/GPT KNOWLEDGE CUTOFFS & PRE-TRAINING TIMELINES

Summary from the TLDR Dev newsletter

Researchers analyze frontier models using probing techniques like date-based prompts and niche fact assessments to reveal insights into their training processes and knowledge cutoffs. By evaluating models' self-reported identities and error rates on historical quizzes, it is possible to estimate the specific timelines of their training data.