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    <abstract>Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate over-smoothing and enhance performance. Extensive experiments on 22 benchmark datasets demonstrate that SIBO significantly enhances the performance of various strong baselines, achieving up to 15.7% and 23.5% improvement over existing PEFT methods on the arithmetic and commonsense reasoning tasks, respectively.</abstract>
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%0 Conference Proceedings
%T SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
%A Wen, Zhihao
%A Zhang, Jie
%A Fang, Yuan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F wen-etal-2024-sibo
%X Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate over-smoothing and enhance performance. Extensive experiments on 22 benchmark datasets demonstrate that SIBO significantly enhances the performance of various strong baselines, achieving up to 15.7% and 23.5% improvement over existing PEFT methods on the arithmetic and commonsense reasoning tasks, respectively.
%U https://aclanthology.org/2024.findings-acl.72
%P 1241-1257

Markdown (Informal)

[SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning](https://aclanthology.org/2024.findings-acl.72) (Wen et al., Findings 2024)

• SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning (Wen et al., Findings 2024)

ACL

• Zhihao Wen, Jie Zhang, and Yuan Fang. 2024. SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning. In Findings of the Association for Computational Linguistics ACL 2024, pages 1241–1257, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.