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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.03216 |
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| _version_ | 1866914824872525824 |
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| author | Wistuba, Martin Sivaprasad, Prabhu Teja Balles, Lukas Zappella, Giovanni |
| author_facet | Wistuba, Martin Sivaprasad, Prabhu Teja Balles, Lukas Zappella, Giovanni |
| contents | Recent Continual Learning (CL) methods have combined pretrained Transformers with prompt tuning, a parameter-efficient fine-tuning (PEFT) technique. We argue that the choice of prompt tuning in prior works was an undefended and unablated decision, which has been uncritically adopted by subsequent research, but warrants further research to understand its implications. In this paper, we conduct this research and find that the choice of prompt tuning as a PEFT method hurts the overall performance of the CL system. To illustrate this, we replace prompt tuning with LoRA in two state-of-the-art continual learning methods: Learning to Prompt and S-Prompts. These variants consistently achieve higher accuracy across a wide range of domain-incremental and class-incremental benchmarks, while being competitive in inference speed. Our work highlights a crucial argument: unexamined choices can hinder progress in the field, and rigorous ablations, such as the PEFT method, are required to drive meaningful adoption of CL techniques in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need Wistuba, Martin Sivaprasad, Prabhu Teja Balles, Lukas Zappella, Giovanni Machine Learning Artificial Intelligence Recent Continual Learning (CL) methods have combined pretrained Transformers with prompt tuning, a parameter-efficient fine-tuning (PEFT) technique. We argue that the choice of prompt tuning in prior works was an undefended and unablated decision, which has been uncritically adopted by subsequent research, but warrants further research to understand its implications. In this paper, we conduct this research and find that the choice of prompt tuning as a PEFT method hurts the overall performance of the CL system. To illustrate this, we replace prompt tuning with LoRA in two state-of-the-art continual learning methods: Learning to Prompt and S-Prompts. These variants consistently achieve higher accuracy across a wide range of domain-incremental and class-incremental benchmarks, while being competitive in inference speed. Our work highlights a crucial argument: unexamined choices can hinder progress in the field, and rigorous ablations, such as the PEFT method, are required to drive meaningful adoption of CL techniques in real-world applications. |
| title | Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.03216 |