Summary of Efficient Prompt Tuning by Multi-space Projection and Prompt Fusion, By Pengxiang Lan et al.
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
by Pengxiang Lan, Enneng Yang, Yuting Liu, Guibing Guo, Jianzhe Zhao, Xingwei Wang
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an Efficient Prompt Tuning (EPT) method for fine-tuning pre-trained language models, addressing two key challenges in prompt tuning: balancing accuracy and efficiency, and ensuring consistent performance across different downstream tasks. The EPT method decomposes a given soft prompt into a shorter prompt and low-rank matrices, reducing training time while enhancing accuracy by leveraging additional knowledge sources. Additionally, the paper introduces multi-space projection and adaptive combination weights through a gating network to improve consistency. Experimental results on 13 natural language processing tasks show significant improvements over 11 comparison methods, with up to 12.9% relative improvement and a 14% reduction in training time. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding human language. Right now, there are ways to teach computers new things without retraining them from scratch, but they can be slow or not very good. The researchers came up with a new way called Efficient Prompt Tuning (EPT) that makes it faster and more accurate. They broke down the old way into smaller parts, which helped. They also found a way to make sure the computer is good at understanding different kinds of language tasks, like summarizing or translating. The tests showed that their method worked really well and was much faster than others. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Prompt