Small Data, Big Noise: Adversarial Training for Robust ParameterEfficient Fine-Tuning (2026.findings-acl)
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| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) is essential for adapting foundation models to downstream tasks, but current methods struggle with robustness to noise and performance degradation on limited training data. |
| Approach: | They propose a framework that brings adversarial training to PEFT to enhance model robustness and generalization, outperforming alternative approaches. |
| Outcome: | Experiments with two variants of the proposed framework show that it outperforms existing methods in low-resource settings and under word-level and character-level corruptions. |
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| Challenge: | Parameter-efficient fine-tuning of pre-trained language models has been demonstrated to be effective, but its inherent characteristics limit its performance. |
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