Summary of Prompt Tuning Strikes Back: Customizing Foundation Models with Low-rank Prompt Adaptation, by Abhinav Jain et al.
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
by Abhinav Jain, Swarat Chaudhuri, Thomas Reps, Chris Jermaine
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 fine-tuning Foundation Models (FMs) called LoPA (Low-Rank Prompt Adaptation) is proposed, which leverages prompt tuning to achieve state-of-the-art performance while being more parameter-efficient and not requiring a server-based adapter. LoPA generates soft prompts by balancing task-specific information sharing across instances with customization for each instance, utilizing a low-rank decomposition of the soft-prompt component encoded for each instance. The method is evaluated on multiple natural language understanding and code generation and understanding tasks across various foundation models with different sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoPA is a new way to customize Foundation Models (FMs) without needing a lot of extra storage or processing power. It does this by adjusting the prompts that are given to the model, rather than creating specialized “adapters” for each task. LoPA performs just as well as other methods, but uses fewer parameters and doesn’t require a powerful server to run on. |
Keywords
» Artificial intelligence » Fine tuning » Language understanding » Parameter efficient » Prompt
