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arXiv Machine Learning By Edith Cohen, Vadym Doroshenko, Badih Ghazi, Charlie Harrison, Pritish Kamath, Alexander Knop, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Mikhail Pravilov, Adam Sealfon, Yurii Sushko, Marika Swanberg, Chiyuan Zhang

CALLIOPE: Private Insights into AI Use, Without Embeddings

Read the original on arXiv Machine Learning →

CALLIOPE is a new top‑down framework designed to uncover topic hierarchies in large unstructured datasets, such as LLM conversations, while ensuring differential privacy. Unlike conventional methods that depend on clustering vector embeddings, CALLIOPE directly employs LLM capabilities throughout its pipeline. Experiments show that CALLIOPE delivers higher utility than embedding‑based approaches, particularly in high‑privacy scenarios, and its outputs are evaluated using both standard clustering metrics and newly introduced utility metrics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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