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New Google DeepMind paper finds that having an LLM build topic trees from keywords, not embeddings, gives clearer topic maps under strict privacy.
Teams want to know what users ask chatbots without exposing anyone. The usual private approach clusters embeddings with added noise, and under strict privacy it returns too few clusters with weak names.
CALLIOPE has an LLM tag each document with a few keywords and adds noise so only common keywords survive. The LLM then proposes topics from them and sorts documents into those topics.
At the strictest privacy setting tested, human raters preferred CALLIOPE over the embedding-based URANIA on all 3 datasets, by up to 76% versus 18%.
The method uses 2 to 5 times more tokens. For privacy-safe usage reports, start with LLM-built topics and budget for that cost.