LATIDIA · Ciberseguridad

CALLIOPE: información privada sobre el uso de la IA, sin incrustaciones

arXiv:2610.28797v1 Announce Type: cross Resumen: Presentamos CALLIOPE, un marco descendente para descubrir jerarquías de temas en grandes grupos de datos no estructurados (por ejemplo, conversaciones de LLM), al tiempo que admite

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arXiv:2610.28797v1 Announce Type: cross 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.