PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification

• URL: http://arxiv.org/abs/2409.17834v1
• Date: Thu, 26 Sep 2024 13:36:00 GMT
• Title: PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification
• Authors: Tianfang Xie, Tianjing Li, Wei Zhu, Wei Han, Yi Zhao,
• Abstract summary: We introduce a new and straightforward PEFT methodology named underlinePrompt DunderlineEpenunderlineDent underlineRepresentation MunderlineOdification (PEDRO)
• Score: 8.312232079766076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM backbone must cater to multiple users or tasks through the application of various parameter-efficient fine-tuning (PEFT) models. Despite the availability of numerous effective PEFT techniques such as LoRA, there remains a need for a PEFT approach that achieves both high efficiency during inference and competitive performance on downstream tasks. In this research, we introduce a new and straightforward PEFT methodology named \underline{P}rompt D\underline{E}pen\underline{D}ent \underline{R}epresentation M\underline{O}dification (PEDRO). The proposed method involves integrating a lightweight vector generator into each Transformer layer, which generates vectors contingent upon the input prompts. These vectors then modify the hidden representations created by the LLM through a dot product operation, thereby influencing the semantic output and generated content of the model. Extensive experimentation across a variety of tasks indicates that: (a) PEDRO surpasses recent PEFT benchmarks when using a similar number of tunable parameters. (b) Under the single-backbone multi-tenant deployment model, PEDRO exhibits superior efficiency compared to LoRA, indicating significant industrial potential.

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