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 methods have combined pretrained Transformers with prompt tuning%2C a parameter-efficient fine-tuning (PEFT) technique. We argue that the choice of prompt tuning in prior works was an undefended and unablated decision%2C which has been uncritically adopted by subsequent research%2C but warrants further research to understand its implications. In this paper%2C 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%2C 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%2C while being competitive in inference speed. Our work highlights a crucial argument: unexamined choices can hinder progress in the field%2C and rigorous ablations%2C such as the PEFT method%2C are required to drive meaningful adoption of CL techniques in real-world applications.?quality=80&w=800)
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