Summary of Assessing the Portability Of Parameter Matrices Trained by Parameter-efficient Finetuning Methods, By Mohammed Sabry and Anya Belz
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods
by Mohammed Sabry, Anya Belz
First submitted to arxiv on: 25 Jan 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 |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the portability of whole functional modules that encode task-specific knowledge from one language model to another. It designs a study with 1,440 training/testing runs to test the portability of modules trained using parameter-efficient finetuning (PEFT) techniques for sentiment analysis. The results show that ported modules far outperform those trained from scratch or sampled from the same distribution as the ported module. The paper highlights performance differences between four PEFT techniques and suggests that task-specific knowledge is highly portable, but its success depends on the type of PEFT and differences between the originating and receiving pretrained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how to move whole functional modules from one language model to another. Researchers tested if they could reuse modules trained for a specific task in a new setting. They used techniques like finetuning to make this possible. The results show that these modules are very useful, but the best way to use them depends on the technique and the models involved. |
Keywords
* Artificial intelligence * Language model * Parameter efficient
