Topics Without Coordinates: What CALLIOPE Gains, and What It Costs
TL;DR for operators A company analyzing thousands of support conversations needs categories that explain what customers are discussing without exposing their individual messages. Conventional systems commonly group numerical representations of messages, then generate descriptions for the resulting clusters. But geometric similarity does not necessarily produce the most informative topic labels. CALLIOPE1 offers an alternative: LLMs recursively summarize documents, synthesize categories from aggregated keywords, and assign records to those categories without embedding-based clustering. Its private version provides a formal document-level differential-privacy guarantee for released topic hierarchies. ...