Summary of Plug-and-play Transformer Modules For Test-time Adaptation, by Xiangyu Chang et al.
Plug-and-Play Transformer Modules for Test-Time Adaptation
by Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit K. Roy-Chowdhury
First submitted to arxiv on: 6 Jan 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 adapting transformer models to new domains is presented in this paper, which leverages parameter-efficient tuning (PET) methods like LoRA, Adapter, and Visual Prompt Tuning (VPT). The key challenge addressed is the need to adapt to a large number of domains during test time, with typically unlabeled data. To overcome this hurdle, the authors introduce PLUTO: a Plug-and-pLay modUlar Test-time domain adaptatiOn strategy that pre-trains a set of modules for different source domains, allowing for efficient selection and combination at test time. The proposed method is shown to outperform alternative approaches in comprehensive evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PLUTO is a new way to help big language models like transformers work well with new kinds of data they haven’t seen before. This can be tricky because the model might not have been trained on that type of data, and there’s usually no labeled information available. To solve this problem, researchers developed PLUTO: a strategy that lets them quickly adapt the model to a new kind of data. They do this by training many smaller models for different types of data, then choosing the best ones to use when working with new data. This makes it possible to get good results even when there’s not much labeled information available. |
Keywords
* Artificial intelligence * Domain adaptation * Lora * Parameter efficient * Prompt * Transformer
