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arXiv · 2610.28797

CALLIOPE: Private Insights into AI Use, Without Embeddings

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
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Abstract

We introduce CALLIOPE, a top-down framework for discovering topic hierarchies in large corpora of unstructured data (e.g., LLM conversations), while supporting differential privacy (DP). Unlike traditional and even recent LLM-augmented approaches that ultimately rely on clustering vector embeddings, our framework leverages LLM capabilities directly for the entire pipeline. Experiments demonstrate that this approach provides better utility over existing (embedding-based) methods in the DP setting, especially in the high privacy regime. We evaluate the quality of CALLIOPE outputs using a variety of traditionally studied clustering metrics, as well as some novel utility metrics that we introduce.

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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. 2026-10-20. CALLIOPE: Private Insights into AI Use, Without Embeddings. https://arxiv.org/abs/2610.28797

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