mlsanityData to 13 Oct 2026

Conf-Gen: Conformal Uncertainty Quantification for Generative Models

arXiv 2605.28920 ↗First posted 27 May 2026cs.LG cs.AI stat.MLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 42 of its 61 references that are in the map. First posted on arXiv 27 May 2026.

Builds on

42 of 61 references.

References · 42

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30 more
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Improved Confidence Estimates for Black-Box Large Language Modelscited by 0