ReBaPL: Repulsive Bayesian Prompt Learning
ReBaPL integrates cyclical SGHMC and representation-space repulsion to enhance multi-modal prompt Bayesian inference, capturing multi-peak posteriors for better generalization.
Key Findings
Methodology
ReBaPL employs a cyclical stochastic gradient Hamiltonian Monte Carlo (rcSGHMC) algorithm, combining cyclical learning rate schedules with a repulsive potential based on distributional distances (MMD, Wasserstein). It models the prompt posterior as a multi-modal distribution, using representation-based repulsion to diversify exploration. This modular Bayesian extension can be applied to any maximum likelihood prompt method, improving the capture of complex posterior landscapes and enhancing generalization, especially in few-shot and out-of-distribution scenarios.
Key Results
- On datasets like ImageNet, Caltech101, and OxfordPets, ReBaPL outperformed baseline prompt methods with an average accuracy increase of 1.2% to 2.5%. Notably, on FGVCAircraft and EuroSAT, improvements exceeded 3%. The multi-peak exploration enabled the models to better handle unseen classes and domain shifts, with significant gains in few-shot (16-shot) setups.
- In experiments combining ReBaPL with MaPLe and MMRL, average accuracy improved by 0.8% to 1.5% across base and novel classes. The repulsive mechanism prevented mode collapse, resulting in higher diversity and robustness, especially in challenging transfer tasks.
Significance
This work advances Bayesian prompt learning by moving beyond unimodal, single-peak approximations. By effectively capturing multi-modal posteriors, ReBaPL enhances model robustness and uncertainty estimation, addressing key limitations of prior variational and particle-based methods. Its modular design facilitates integration into existing prompt frameworks, broadening the scope of reliable, generalizable foundation models for real-world applications such as zero-shot classification, cross-domain adaptation, and few-shot learning.
Technical Contribution
The primary innovations include the integration of a representation-space-based repulsive potential within a cyclical SGHMC sampler, enabling efficient exploration of complex, multi-modal posterior landscapes. The method leverages distributional distances (MMD, Wasserstein) to quantify prompt diversity, overcoming high-dimensional parameter space challenges. It offers a flexible, plug-and-play Bayesian extension for any MLE-based prompt learning, significantly improving exploration and generalization capabilities.
Novelty
This is the first work to incorporate a representation-space repulsive potential into Bayesian prompt inference, combined with cyclical SGHMC sampling, to effectively explore multi-modal posteriors. Unlike prior unimodal variational approaches, ReBaPL explicitly promotes diversity among prompt samples, capturing multiple plausible modes in the posterior distribution, thus enabling more robust and generalizable prompt representations.
Limitations
- The computational overhead of distance calculations (MMD, Wasserstein) and multi-sample sampling can be significant, especially for large-scale datasets or high-dimensional prompts, limiting real-time applications.
- The approach relies on quality representations; poor feature extraction may weaken the effectiveness of the repulsive force, leading to suboptimal exploration.
- Validation primarily in few-shot settings; its efficacy in extremely low-data regimes or highly complex tasks remains to be thoroughly tested.
Future Work
Future directions include optimizing distance computations for scalability, integrating adaptive distance metrics, and extending the framework to end-to-end multi-modal large models. Further research will explore explainability of multi-modal prompt modes, and applying the approach to broader tasks like generative modeling and reinforcement learning, aiming to improve robustness and uncertainty quantification in real-world AI systems.
AI Executive Summary
Prompt learning has revolutionized the adaptation of large-scale foundation models, enabling efficient task-specific tuning without full model retraining. However, conventional methods relying on maximum likelihood estimation often overfit, limiting their ability to generalize to out-of-distribution data. Bayesian prompt learning offers a promising alternative by modeling the prompt space as a posterior distribution, capturing uncertainty and diversity. Yet, existing approaches typically approximate this posterior as unimodal, missing the rich multi-modal structure inherent in complex prompt landscapes.
This paper introduces ReBaPL, a novel Bayesian prompt learning framework that leverages a cyclical stochastic gradient Hamiltonian Monte Carlo (rcSGHMC) algorithm combined with a representation-space repulsive potential. The core idea is to explore multiple modes of the prompt posterior efficiently, encouraging diverse prompt samples that better reflect the true underlying distribution. The repulsive potential, based on distributional distances such as MMD and Wasserstein, prevents the sampler from collapsing into a single mode, thus capturing the multi-peak structure of the posterior.
Experimental results across various datasets, including ImageNet, Caltech101, and OxfordPets, demonstrate that ReBaPL consistently outperforms state-of-the-art prompt learning methods. In few-shot settings, it improves accuracy by 1.2% to 2.5%, with notable gains on challenging datasets like EuroSAT and FGVCAircraft. The method’s ability to uncover multiple plausible prompt modes enhances robustness in zero-shot and domain transfer tasks, addressing key limitations of prior unimodal or single-peak Bayesian approaches.
Overall, ReBaPL represents a significant step forward in Bayesian prompt learning, providing a flexible, modular, and more expressive framework for capturing the complex posterior landscape. Its capacity to improve generalization and uncertainty estimation makes it a promising tool for deploying foundation models in real-world, data-scarce scenarios. Future work will focus on scalability, adaptive metrics, and broader multi-modal applications, aiming to push the boundaries of robust, generalizable AI systems.
Deep Dive
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.