Summary of Choice Of Peft Technique in Continual Learning: Prompt Tuning Is Not All You Need, by Martin Wistuba and Prabhu Teja Sivaprasad and Lukas Balles and Giovanni Zappella
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need
by Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella
First submitted to arxiv on: 5 Jun 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 The paper investigates the implications of using prompt tuning in Continual Learning (CL) methods that combine pretrained Transformers with parameter-efficient fine-tuning (PEFT) techniques. It argues that the choice of prompt tuning has been uncritically adopted and warrants further research to understand its impact. The study finds that replacing prompt tuning with LoRA in two state-of-the-art CL methods improves their overall performance on domain-incremental and class-incremental benchmarks, while maintaining competitive inference speed. This highlights the importance of rigorous ablations and examining unexamined choices in driving meaningful adoption of CL techniques in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into how a specific way of fine-tuning language models called prompt tuning affects their performance. Currently, many researchers are using this method without thinking about its implications. The study shows that if they stop using it and instead use another approach called LoRA, the models will get better results on certain tasks while still being fast enough to use in real-life applications. |
Keywords
» Artificial intelligence » Continual learning » Fine tuning » Inference » Lora » Parameter efficient » Prompt
