自适应分类器自由引导：基于动态低置信度掩码
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
AI总结：
针对标准CFG静态无条件输入在迭代生成中的不足，提出自适应分类器自由引导（A-CFG），通过动态低置信度掩码定制无条件输入，在掩码扩散语言模型上显著提升生成质量，如GPQA提升3.9分。
摘要：
分类器自由引导（CFG）通过插值条件预测和无条件预测，显著增强了生成模型的可控性。然而，标准CFG通常采用静态的无条件输入，这在模型不确定性动态变化的迭代生成过程中可能不是最优的。我们引入了自适应分类器自由引导（A-CFG），一种新颖的方法，利用模型即时的预测置信度来定制无条件输入。在迭代（掩码）扩散语言模型的每一步中，A-CFG识别当前生成序列中模型表现出低置信度的token。这些token被暂时重新掩码，以创建动态的、局部的无条件输入。这将CFG的纠正影响精确地集中在模糊区域，从而实现更有效的引导。我们将A-CFG集成到一个最先进的掩码扩散语言模型中，并证明了其有效性。在多种语言生成基准上的实验表明，A-CFG相比标准CFG带来了显著的改进，例如在GPQA上获得了3.9分的提升。我们的工作强调了在迭代生成中动态调整引导机制以适应模型不确定性的好处。
英文摘要：
Classifier-Free Guidance (CFG) significantly enhances controllability in generative models by interpolating conditional and unconditional predictions. However, standard CFG often employs a static unconditional input, which can be suboptimal for iterative generation processes where model uncertainty varies dynamically. We introduce Adaptive Classifier-Free Guidance (A-CFG), a novel method that tailors the unconditional input by leveraging the model's instantaneous predictive confidence. At each step of an iterative (masked) diffusion language model, A-CFG identifies tokens in the currently generated sequence for which the model exhibits low confidence. These tokens are temporarily re-masked to create a dynamic, localized unconditional input. This focuses CFG's corrective influence precisely on areas of ambiguity, leading to more effective guidance. We integrate A-CFG into a state-of-the-art masked diffusion language model and demonstrate its efficacy. Experiments on diverse language generation benchmarks show that A-CFG yields substantial improvements over standard CFG, achieving, for instance, a 3.9 point gain on GPQA. Our work highlights the benefit of dynamically adapting guidance mechanisms to model uncertainty in iterative generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
