Conf-Gen: Conformal Uncertainty Quantification for Generative Models
Gabriel Loaiza-Ganem, Kevin Zhang, Wei Cui, Marc Law, Kin Kwan Leung
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.
BibTeX
@inproceedings{
loaiza-ganem2026confgen,
title={Conf-Gen: Conformal Uncertainty Quantification for Generative Models},
author={Gabriel Loaiza-Ganem and Kevin Zhang and Wei Cui and Marc T. Law and Kin Kwan Leung},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=moI2fePoWA}
}