Dynamic Prompting: A Unified Framework for Prompt Tuning

It has been demonstrated that the art of prompt tuning is highly effective in efficiently extracting knowledge from pretrained foundation models, encompassing pretrained language models (PLMs), vision pretrained models, and vision-language (V-L) models. However, the efficacy of employing fixed soft prompts with a predetermined position for concatenation with inputs for all instances, irrespective of their inherent disparities, remains uncertain. Variables such as the position, length, and representations of prompts across diverse instances and tasks can substantially influence the performance of prompt tuning. In this context, we provide a theoretical analysis, which reveals that optimizing the position of the prompt to encompass the input can capture additional semantic information that traditional prefix or postfix prompt tuning methods fail to capture. Building upon our analysis, we present a unified dynamic prompt (DP) tuning strategy that dynamically determines different factors of prompts based on specific tasks and instances. To accomplish this, we employ a lightweight learning network with Gumble-Softmax, allowing us to learn instance-dependent guidance. Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks. Furthermore, we establish the universal applicability of our approach under full-data, few-shot, and multitask scenarios. Codes are available at

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Summary Notes

Revolutionizing AI Model Efficiency with Dynamic Prompting

Traditional vs. Dynamic Prompting

Limitations of Traditional Prompt Tuning

• Uses fixed prompts, limiting flexibility and optimization.

• Can hinder model performance and generalization across various tasks.

Advantages of Dynamic Prompting

• Introduces flexibility in prompt parameters (position, length, and representation).

• Enhances model's semantic extraction capability across different tasks.

Exploring the Dynamic Prompting Framework

Key Features

• Adaptive Position and Length: Allows for tailored information extraction by adjusting prompt parameters based on the task.

• Adaptive Prompt Vector: Employs a dynamic selection of prompt vectors to align with each instance's nuances.

Implementation Overview

• Utilizes a one-layer feedforward network with the Gumbel-Softmax technique for efficient learning and adjustment of prompt parameters.

Benefits and Implications

Evidence of Superiority

• Experiments using the OpenPrompt framework and T5 model sizes on SuperGLUE datasets show notable improvements in performance.

Significance for Enterprise AI Engineers

• Enhanced Efficiency: Dynamic prompting leads to higher performance, lower computational costs, and faster adaptation.

• Increased Flexibility: Offers adaptable AI solutions for the diverse needs of enterprise environments.

• Competitive Advantage: Provides an edge in developing AI-driven products and services.

Conclusion: The Future of Prompt Tuning

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