Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
<|reference_start|>Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation: Parameter-Efficient Fine-Tuning (PEFT) has become the standard for customising Foundation Models (FMs) to user-specific downstream tasks. However, typical PEFT methods require storing multiple task-specific adapters, creating scalability issues as these adapters must be housed and run at the FM server. Traditional prompt tuning offers a potential solution by customising them through task-specific input prefixes, but it under-performs compared to other PEFT methods like LoRA. To address this gap, we propose Low-Rank Prompt Adaptation (LOPA), a prompt-tuning-based approach that performs on par with state-of-the-art PEFT methods and full fine-tuning while being more parameter-efficient and not requiring a server-based adapter. LOPA generates soft prompts by balancing between sharing task-specific information across instances and customization for each instance. It uses a low-rank decomposition of the soft-prompt component encoded for each instance to achieve parameter efficiency. We provide a comprehensive evaluation on multiple natural language understanding and code generation and understanding tasks across a wide range of foundation models with varying sizes.<|reference_end|>
@article{jain2024prompt,
    title={Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank
  Prompt Adaptation},
    author={Abhinav Jain, Swarat Chaudhuri, Thomas Reps, Chris Jermaine},
    journal={arXiv preprint arXiv:2405.15282},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15282},
    primaryClass={cs.LG cs.AI}
}