为生成模型提供可信赖的不确定性量化方法，让AI输出更可信。
Conf-Gen: Conformal Uncertainty Quantification for Generative Models
- 将校准预测扩展到生成任务，适配大模型与图像生成。
- 在非记忆化图像生成、对话澄清、智能体输出正确性上实现保证。
- 突破传统约束，适用于新场景，适合追求AI可信性的研究者。
校准预测（CP）及其扩展形式校准风险控制（CRC）是监督学习中量化不确定性的成熟框架，提供形式化保障。然而，近年来人工智能的重大进展主要来自无监督生成模型，如大型语言模型（LLMs）和图像生成器，这些模型无法直接适用CP或CRC。本文提出校准生成（Conf-Gen），一个通用框架，将CRC适配至生成任务，同时放宽其理论假设。Conf-Gen统一并推广了此前将CP应用于LLMs的尝试，并将校准方法拓展至全新领域。我们通过若干新颖应用展示了Conf-Gen的灵活性，包括对生成非记忆化图像的图像生成器、要求足够澄清问题的对话AI系统、以及智能体输出正确的性提供校准保证。
原文摘要 · Abstract (English)
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.
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