AI RESEARCH
Conf-Gen: Conformal Uncertainty Quantification for Generative Models
arXiv CS.AI
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ArXi:2605.28920v1 Announce Type: cross 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