Papers with BOoster

1 papers
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning (2024.findings-acl)

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Challenge: Recent advances in parameter-efficient fine-tuning (PEFT) techniques allow for adjustments to only a minor fraction of the parameters of large language models.
Approach: They propose a SImple BOoster to enhance parameter-efficient fine-tuning techniques by injecting an initial residual into the model.
Outcome: The proposed model improves performance on 22 benchmark datasets and can be extended to a range of state-of-the-art techniques.

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