LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models

TL;DR

LoPT introduces a low-rank prompt tuning method that significantly reduces trainable parameters while maintaining high performance, enhancing parameter efficiency for task-specific language model adaptation.

Contribution

The paper proposes Low-rank Prompt Tuning (LoPT), a novel approach that reduces trainable parameters by a factor of 5 compared to full prompt tuning, with competitive results.

Findings

Achieves similar performance to full prompt tuning

Reduces trainable parameters by a factor of 5

Outperforms some state-of-the-art methods with fewer parameters

Abstract

In prompt tuning, a prefix or suffix text is added to the prompt, and the embeddings (soft prompts) or token indices (hard prompts) of the prefix/suffix are optimized to gain more control over language models for specific tasks. This approach eliminates the need for hand-crafted prompt engineering or explicit model fine-tuning. Prompt tuning is significantly more parameter-efficient than model fine-tuning, as it involves optimizing partial inputs of language models to produce desired outputs. In this work, we aim to further reduce the amount of trainable parameters required for a language model to perform well on specific tasks. We propose Low-rank Prompt Tuning (LoPT), a low-rank model for prompts that achieves efficient prompt optimization. The proposed method demonstrates similar outcomes to full parameter prompt tuning while reducing the number of trainable parameters by a factor of 5. It also provides promising results compared to the state-of-the-art methods that would require 10 to 20 times more parameters.