Differentiable Prompt Learning for Vision Language Models

TL;DR

This paper introduces Differentiable Prompt Learning (DPL), an automated method to optimize prompt configurations in vision-language models, significantly improving downstream task performance over manual prompt strategies.

Contribution

The paper proposes DPL, a novel optimization-based approach for automatic prompt configuration, enhancing deep continuous prompt design in pre-trained models.

Findings

DPL achieves a 2.60% average accuracy boost on 11 datasets.

DPL effectively finds high-confidence prompt configurations with limited data.

The method is compatible with existing sophisticated prompt designs.

Abstract

Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning them into differentiable vectors. Deep continuous prompts insert prompts not only in the input but also in the intermediate hidden representations. Manually designed deep continuous prompts exhibit a remarkable improvement compared to the zero-shot pre-trained model on downstream tasks. How to automate the continuous prompt design is an underexplored area, and a fundamental question arises, is manually designed deep prompt strategy optimal? To answer this question, we propose a method dubbed differentiable prompt learning (DPL). The DPL method is formulated as an optimization problem to automatically determine the optimal context length of the prompt to be added to each layer, where the objective is to maximize the performance. We test the DPL method on the pre-trained CLIP. We empirically find that by using only limited data, our DPL method can find deep continuous prompt configuration with high confidence. The performance on the downstream tasks exhibits the superiority of the automatic design: our method boosts the average test accuracy by 2.60% on 11 datasets compared to baseline methods. Besides, our method focuses only on the prompt configuration (i.e. context length for each layer), which means that our method is compatible with the baseline methods that have sophisticated designs to boost the performance. The DPL method can be deployed to large language models or computer vision models at no cost.