{"slug": "calliope-private-insights-into-ai-use-without-embeddings", "title": "CALLIOPE: Private Insights into AI Use, Without Embeddings", "summary": "Researchers posted CALLIOPE to arXiv on 20 Oct 2026, a top-down framework that uses LLMs directly across the entire pipeline to discover topic hierarchies in large unstructured corpora such as LLM conversations while supporting differential privacy. The authors report that CALLIOPE delivers better utility than existing embedding-based methods in the DP setting, especially in the high privacy regime, and evaluate its outputs with traditional clustering metrics plus novel utility metrics they introduce. The work matters because it offers a way to surface insights into AI use without relying on vector embeddings.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 20 Oct 2026]\n\n# Title:CALLIOPE: Private Insights into AI Use, Without Embeddings\n\n[View PDF](https://arxiv.org/pdf/2610.28797)\n\n[HTML (experimental)](https://arxiv.org/html/2610.28797v1)\n\nAbstract: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.\n    \n\n### Additional Features\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/calliope-private-insights-into-ai-use-without-embeddings", "canonical_source": "https://arxiv.org/abs/2610.28797", "published_at": "2026-10-22 04:00:00+00:00", "updated_at": "2026-10-22 04:13:59.065724+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research", "ai-safety"], "entities": ["CALLIOPE", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/calliope-private-insights-into-ai-use-without-embeddings", "markdown": "https://wpnews.pro/news/calliope-private-insights-into-ai-use-without-embeddings.md", "text": "https://wpnews.pro/news/calliope-private-insights-into-ai-use-without-embeddings.txt", "jsonld": "https://wpnews.pro/news/calliope-private-insights-into-ai-use-without-embeddings.jsonld"}}