# One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning

**Authors:** Doyoung Kim, Susik Yoon, Dongmin Park, Youngjun Lee, Hwanjun Song, Jihwan Bang, Jae-Gil Lee  
**arXiv:** [2311.12048](https://arxiv.org/abs/2311.12048)  
**Date:** 2023-11-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.12048v1/

## Key points

- Managing prompts in continual learning is a critical challenge due to intricate and unpredictable semantic shifts.
- Traditional prompting strategies are not effective in addressing the dynamic nature of these changes.
- SemPrompt is introduced as an innovative adaptive prompting approach tailored to each task's semantics through a two-level semantic grouping process.
- SemPrompt ensures optimal utilization of prompts across diverse task semantics by dynamically adjusting them based on evolving semantics.
- Through rigorous experimentation, SemPrompt consistently outperforms existing methods in adapting to a wide range of semantic shifts within tasks.
- Its adaptive capabilities and nuanced prompt management strategies make SemPrompt a promising solution for navigating complexities in continual learning settings.

## Summary

In the realm of continual learning, managing prompts is a critical challenge due to intricate and unpredictable semantic shifts. Traditional approaches using universal or specific prompting strategies fall short in addressing the dynamic nature of these changes. To overcome this limitation, SemPrompt has been introduced as an innovative adaptive prompting approach. This methodology tailors minimal yet sufficient prompts based on the nuances of each task's semantics through a sophisticated two-level semantic grouping process. By adopting this comprehensive approach, SemPrompt ensures optimal utilization of prompts across diverse task semantics. Through rigorous experimentation and evaluation, it has consistently outperformed existing methods in adapting to a wide range of semantic shifts within tasks. Its ability to dynamically adjust prompts based on evolving semantics enhances the efficiency and effectiveness of learning processes in real-world scenarios. With its adaptive capabilities and nuanced prompt management strategies, SemPrompt stands as a promising solution for navigating the complexities posed by semantic shifts in continual learning settings.