Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt tuning methods are prone to overfitting and can struggle with out-of-distribution generalization. To address these limitations, Bayesian prompt learning has been proposed, which frames prompt optimization as a Bayesian inference problem to enhance robustness. This paper introduces Repulsive Bayesian Prompt Learning (ReBaPL), a novel method for Bayesian prompt learning, designed to efficiently explore the complex and often multimodal posterior landscape of prompts. Our method integrates a cyclical step-size schedule with a stochastic gradient Hamiltonian Monte Carlo (SGHMC) algorithm, enabling alternating phases of exploration to discover new modes, and exploitation to refine existing modes. Furthermore, we introduce a repulsive force derived from a potential function over probability metrics (including Maximum Mean Discrepancy and Wasserstein distance) computed on the distributions of representations produced by different prompts. This representation-space repulsion diversifies exploration and prevents premature collapse to a single mode. Our approach allows for a more comprehensive characterization of the prompt posterior distribution, leading to improved generalization. In contrast to prior Bayesian prompt learning methods, our method provides a modular plug-and-play Bayesian extension of any existing prompt learning method based on maximum likelihood estimation. We demonstrate the efficacy of ReBaPL on several benchmark datasets, showing superior performance over state-of-the-art methods for prompt learning.

翻译：提示学习已成为针对下游任务微调大规模基础模型的有效技术。然而，传统的提示调优方法容易过拟合，且在分布外泛化方面可能面临困难。为应对这些局限性，贝叶斯提示学习被提出，它将提示优化构建为贝叶斯推断问题以增强鲁棒性。本文介绍了一种新颖的贝叶斯提示学习方法——排斥性贝叶斯提示学习（ReBaPL），旨在高效探索提示复杂且常为多模态的后验分布空间。该方法将循环步长调度与随机梯度哈密顿蒙特卡洛（SGHMC）算法相结合，实现了探索新模态与优化现有模态的交替阶段。此外，我们引入了一种基于概率度量（包括最大均值差异和Wasserstein距离）势函数导出的排斥力，该度量计算自不同提示产生的表示分布。这种表示空间排斥力促进了探索的多样性，并防止过早坍缩至单一模态。我们的方法能够更全面地刻画提示后验分布，从而提升泛化性能。与先前的贝叶斯提示学习方法相比，本方法为任何基于最大似然估计的现有提示学习方法提供了模块化的即插即用贝叶斯扩展。我们在多个基准数据集上验证了ReBaPL的有效性，其性能优于当前最先进的提示学习方法。