Edith Cohen, Vadym Doroshenko, Badih Ghazi, Charlie Harrison, Pritish Kamath, Alexander Knop, Ravi Kumar, Ethan Leeman · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28797 · open-access PDF
我们介绍了CALLIOPE,一个向上而下的框架,用于在大型非结构化数据(例如,LLM对话)中发现主题层次,同时支持差异隐私(DP)。与传统甚至最近的LLM扩展方法不同,这些方法最终依赖于聚合矢量嵌入,我们的框架直接利用LLM功能用于整个管道。实验表明,这种方法在DP设置中比现有(嵌入式)方法更有用,特别是在高隐私模式中。
Machine translation (Meta M2M100); the English original follows.
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
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