Summary of Towards Optimal Adapter Placement For Efficient Transfer Learning, by Aleksandra I. Nowak et al.
Towards Optimal Adapter Placement for Efficient Transfer Learning
by Aleksandra I. Nowak, Otniel-Bogdan Mercea, Anurag Arnab, Jonas Pfeiffer, Yann Dauphin, Utku Evci
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Parameter-efficient transfer learning (PETL) aims to adapt pre-trained models to new tasks while minimizing the number of fine-tuned parameters. Adapters, a popular PETL approach, inject additional capacity by incorporating low-rank projections, achieving performance comparable to full fine-tuning with fewer parameters. This paper investigates how adapter placement affects its performance and introduces an extended search space for adapter connections, including long-range and recurrent adapters. Our findings show that even randomly selected adapter placements from this expanded space yield improved results, and high-performing placements often correlate with high gradient rank. The optimal adapter placement is task-dependent, and a small number of strategically placed adapters can match or exceed the performance of adding adapters in every block. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make computers learn new things by building on what they already know. They want to do this with as few changes as possible. One way to do this is called “adapters”. These adapters help computers understand new information better. The problem is that where you put the adapter makes a big difference in how well it works. This paper shows that even just picking random places for adapters can make them work really well. They also found that some tasks are better suited to certain adapter placements. Overall, this research opens up new ways to help computers learn and adapt. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient » Transfer learning
