ReBaPL: Repulsive Bayesian Prompt Learning

TL;DR

ReBaPL introduces a Bayesian prompt learning method that uses a repulsive force and a specialized sampling algorithm to better explore prompt distributions, improving out-of-distribution generalization.

Contribution

It presents a novel, modular Bayesian prompt learning approach combining a repulsive potential with SGHMC for enhanced exploration and diversity.

Findings

ReBaPL outperforms existing prompt learning methods on benchmark datasets.

The repulsive force prevents mode collapse and encourages diverse prompt exploration.

The method improves robustness and generalization in prompt learning.

Abstract

Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning 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 prompt learning methods.