Conf-Gen: Conformal Uncertainty Quantification for Generative Models
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
Title:Conf-Gen: Conformal Uncertainty Quantification for Generative Models
Abstract:Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machine learning through formal guarantees. However, recent breakthroughs in artificial intelligence (AI) have been driven by unsupervised generative models, such as large language models (LLMs) and image generators, which are not directly compatible with CP or CRC. In this work we introduce conformal generation (Conf-Gen), a general framework adapting CRC to generative tasks while relaxing its theoretical assumptions. Conf-Gen unifies and generalizes previous attempts to apply CP to LLMs, and extends conformal methodology to entirely new domains. We demonstrate the flexibility of Conf-Gen through some novel applications, including obtaining conformal guarantees on: image generators producing non-memorized images, conversational AI systems having asked enough clarifying questions, and the output of AI agents being correct.
| Comments: | ICML 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.28920 [cs.LG] |
| (or arXiv:2605.28920v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28920
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Gabriel Loaiza-Ganem [view email][v1] Wed, 27 May 2026 18:00:00 UTC (4,624 KB)
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both 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.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them
May 29
-
Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents
May 29
-
Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
May 29
-
Molecular Lead Optimization via Agentic Tool Planning
May 29
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.