Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning

• URL: http://arxiv.org/abs/2404.11916v2
• Date: Thu, 17 Oct 2024 09:01:27 GMT
• Title: Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning
• Authors: Nakyeong Yang, Jiwon Moon, Junseok Kim, Yunah Jang, Kyomin Jung,
• Abstract summary: We propose a novel prompt tuning framework called Skeleton to efficiently utilize a language model in terms of memory and time complexity. Our method significantly enhances inference efficiency (at most x 1.73 speed up) for various widely used benchmarks.
• Score: 15.695487920048816
• License:
• Abstract: Prompt tuning methods have shown comparable performance to general training methods as parameter-efficient fine-tuning (PEFT) methods in various natural language understanding tasks. However, existing prompt tuning methods still utilize the entire model architecture even when solving a specific task, which prevents them from accelerating inference speed during the application procedure. In this paper, we propose a novel prompt tuning framework called Skeleton to efficiently utilize a language model in terms of memory and time complexity for solving various tasks, retaining only task-relevant neurons by using an explainability method. From our framework, we can efficiently solve various tasks by using only task-relevant neurons and prepending adequate task-specific prompt tokens with only a single language model. Experiments reveal that our method significantly enhances inference efficiency (at most x 1.73 speed up) for various widely used benchmarks, showing comparable performance to the prompt tuning method. Moreover, our method is applicable across various transformer-based architectures, confirming its practicality and scalability.

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