Summary of X-peft: Extremely Parameter-efficient Fine-tuning For Extreme Multi-profile Scenarios, by Namju Kwak and Taesup Kim
X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios
by Namju Kwak, Taesup Kim
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper proposes a novel parameter-efficient fine-tuning (PEFT) method called X-PEFT, which leverages multiple adapters and uses compact tensors as binary masks to adaptively select them. This approach reduces memory requirements per profile by a factor of 10,000 compared to conventional adapter tuning, while matching or surpassing its performance on the LaMP and GLUE tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary X-PEFT is a new way to fine-tune language models for specific tasks or profiles without using too many extra parameters. It works by choosing which adapters to use from a big library of trained and untrained (random) ones. This approach is super efficient, using 1/10,000th the memory needed for traditional adapter tuning. |
Keywords
* Artificial intelligence * Fine tuning * Parameter efficient
