ReBaPL: Repulsive Bayesian Prompt Learning

• URL: http://arxiv.org/abs/2511.17339v1
• Date: Fri, 21 Nov 2025 16:00:13 GMT
• Title: ReBaPL: Repulsive Bayesian Prompt Learning
• Authors: Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma, Gabriel Mahuas, Victoria Shevchenko, Mike Gartrell,
• Abstract summary: This paper introduces Repulsive Bayesian Prompt Learning (ReBaPL), a novel method for Bayesian prompt learning.<n>Our method integrates a cyclical step-size schedule with a posterior gradient Hamiltonian Monte Carlo algorithm.<n>We demonstrate the efficacy of ReBaPL on several benchmark datasets, showing superior performance over state-of-the-art methods for prompt learning.
• Score: 8.744783207923211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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