Key-value pair-free continual learner via task-specific prompt-prototype.
Haihua Luo1, Xuming Ran2, Zhengji Li3
1University of Jyväskylä, Faculty of Information and Technology, Finland; Dalian University of Technology, School of Computer Science and Technology, China.
Summary
This study introduces a new continual learning method using task-specific Prompt-Prototypes (ProP) to avoid key-value pairs. This approach enhances knowledge acquisition and retention without inter-task interference, improving model scalability.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computer Science
Background:
- Continual learning enables models to learn sequentially without forgetting.
- Prompt-based methods are effective but suffer from inter-task interference and scalability issues due to key-value pairing.
Purpose of the Study:
- To propose a novel prompt-based continual learning method that eliminates key-value pair dependency.
- To enhance feature learning and model stability in continual learning scenarios.
Main Methods:
- Introduced task-specific Prompt-Prototypes (ProP) to replace key-value pairs.
- Utilized prompts for effective current-task feature learning and prototypes for representative feature capture.
- Implemented regularization constraints during prompt initialization to improve stability.
Main Results:
- The proposed ProP method demonstrated effectiveness across multiple datasets.
- Eliminated the need for key-value pairs, addressing limitations of existing prompt-based approaches.
- Showcased improved performance in continual learning tasks.
Conclusions:
- The ProP framework offers a scalable and stable alternative for prompt-based continual learning.
- This novel approach provides a new direction for future research in continual learning by removing key-value dependencies.
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