Summary of Prompt Customization For Continual Learning, by Yong Dai and Xiaopeng Hong and Yabin Wang and Zhiheng Ma and Dongmei Jiang and Yaowei Wang
Prompt Customization for Continual Learning
by Yong Dai, Xiaopeng Hong, Yabin Wang, Zhiheng Ma, Dongmei Jiang, Yaowei Wang
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to continual learning is presented, addressing the limitations of traditional prompt selection methods. The proposed Prompt Customization (PC) method consists of a Prompt Generation Module (PGM) and a Prompt Modulation Module (PMM). PGM generates tailored prompts by assigning coefficients to prompts from a fixed-sized pool, while PMM modulates the prompts based on correlations between input data and corresponding prompts. Experimental results on four benchmark datasets demonstrate consistent improvement over state-of-the-art techniques, with up to 16.2% better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning is a way for machines to keep getting better as they learn more things. Usually, this involves giving the machine a list of questions or tasks to solve, but this approach has some problems. One issue is that the machine might get confused by random or noisy inputs. To fix this, researchers came up with a new method called Prompt Customization (PC). This method uses two parts: one that generates prompts and another that adjusts them based on what the machine is learning. They tested this method on four different sets of data and found that it worked better than other methods, improving performance by as much as 16.2%. |
Keywords
» Artificial intelligence » Continual learning » Prompt
