Summary of Lopt: Low-rank Prompt Tuning For Parameter Efficient Language Models, by Shouchang Guo et al.
LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models
by Shouchang Guo, Sonam Damani, Keng-hao Chang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 The paper introduces prompt tuning, a novel approach that enhances control over language models for specific tasks by adding prefixes or suffixes to prompts and optimizing their embeddings or token indices. This method eliminates the need for manual prompt engineering or explicit model fine-tuning, offering significant parameter efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prompt tuning is a technique that helps improve language models’ performance on specific tasks by adjusting the inputs given to them. By adding special text to the beginning or end of prompts, and then changing how the model uses this text, we can control what it does without having to change the model itself. This makes things more efficient because we don’t need to make as many changes. |
Keywords
» Artificial intelligence » Fine tuning » Prompt » Token
