Conf-Gen: Conformal Uncertainty Quantification for Generative Models

2026-05-27

AI Summary

This work addresses the challenge of applying conformal prediction (CP) and conformal risk control (CRC) to unsupervised generative models, where formal uncertainty guarantees for generation tasks have been lacking. The paper proposes Conf-Gen, a novel framework that systematically extends conformal methods to generative settings by relaxing theoretical assumptions and establishing a unified mechanism for uncertainty quantification. Conf-Gen is applicable across diverse scenarios—including large language models, image generation, question clarification in dialogue, and verification of AI agent outputs—and delivers rigorous conformal guarantees. Empirically, it enables formally valid assurances for tasks such as generating non-memorized images, assessing the sufficiency of user queries, and verifying output correctness, thereby substantially broadening the scope of conformal methods in generative AI.

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.

Problem

• conformal prediction
• generative models
• uncertainty quantification
• large language models
• conformal risk control

Innovation

• conformal generation
• generative models
• uncertainty quantification
• conformal risk control
• large language models