New PEFT Method Addresses Small Data, Big Noise
Researchers have developed a new Parameter-Efficient Fine-Tuning (PEFT) method called 'Small Data, Big Noise' to adapt foundation models to downstream NLP tasks. This method focuses on robust training with limited data. This technique aims to improve model performance in noisy environments.
Topics
Developing
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Sources · 7 independent
Modernity/arxiv
“Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning. Authors: Eitan Cohen, Idan Simai, Uri Shaham Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become essential for adapting foundation models to downstream NLP tasks.”
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