Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation

• URL: http://arxiv.org/abs/2405.15282v2
• Date: Thu, 31 Oct 2024 22:29:59 GMT
• Title: Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
• Authors: Abhinav Jain, Swarat Chaudhuri, Thomas Reps, Chris Jermaine,
• Abstract summary: Low-Rank Prompt Adaptation (LoPA) is a prompt-tuning-based approach that performs on par with state-of-the-art PEFT methods and full fine-tuning. 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.
• Score: 13.325756523035245
• License:
• Abstract: 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.

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