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

ACL ARR 2024 June Submission5435 Authors

16 Jun 2024 (modified: 02 Jul 2024)ACL ARR 2024 June SubmissionEveryoneRevisionsBibTeXCC BY 4.0
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.
Paper Type: Short
Research Area: Efficient/Low-Resource Methods for NLP
Research Area Keywords: parameter-efficient-training, prompting, efficiency, Efficiency in Model Algorithms, Training, and Inference
Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency
Languages Studied: English
Submission Number: 5435
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