Google DeepMind's CALLIOPE maps LLM topics under differential privacy
Keyword-based pipelines can beat embeddings when privacy noise is heavy, a counterintuitive result for anyone building usage analytics on sensitive logs. Teams deploying private analytics should budget for the 2-5x token overhead and prototype with LLM-generated topic trees instead of embedding clusters. No regional angle is given, so the practical comparison is with URANIA, the embedding-based baseline it outperformed.
