无需微调即可控制离散掩码模型生成，适用于蛋白设计等任务。
Plug-and-Play Controllable Generation for Discrete Masked Models
- 基于重要性采样构建即插即用框架，不依赖条件得分训练。
- 可在无梯度信息下实现后验采样与约束生成，通用性强。
- 适合需要精准控制的下游任务，如特定类别图像生成、蛋白设计。
本文使离散掩码模型具备可控生成能力，旨在生成满足后验分布、特定约束或优化奖励函数的离散随机变量样本。现有方法通常依赖任务特定微调或额外修改，效率低且资源消耗大。为此，我们提出一种基于重要性采样的新框架，无需训练条件得分，对控制准则选择无感，且不需梯度信息，适用于后验采样、贝叶斯逆问题和约束生成等任务。通过大量实验验证，该方法在蛋白设计等多个领域展现出良好泛化性与实用性。
原文摘要 · Abstract (English)
This article makes discrete masked models for the generative modeling of discrete data controllable. The goal is to generate samples of a discrete random variable that adheres to a posterior distribution, satisfies specific constraints, or optimizes a reward function. This methodological development enables broad applications across downstream tasks such as class-specific image generation and protein design. Existing approaches for controllable generation of masked models typically rely on task-specific fine-tuning or additional modifications, which can be inefficient and resource-intensive. To overcome these limitations, we propose a novel plug-and-play framework based on importance sampling that bypasses the need for training a conditional score. Our framework is agnostic to the choice of control criteria, requires no gradient information, and is well-suited for tasks such as posterior sampling, Bayesian inverse problems, and constrained generation. We demonstrate the effectiveness of our approach through extensive experiments, showcasing its versatility across multiple domains, including protein design.
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