AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning

• URL: http://arxiv.org/abs/2210.05883v1
• Date: Wed, 12 Oct 2022 02:54:41 GMT
• Title: AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
• Abstract summary: We find that dropping attention positions with low attribution scores can accelerate training and increase the risk of overfitting. We develop a cross-tuning strategy to alternate fine-tuning and AD-DROP to avoid dropping high-attribution positions excessively.
• Score: 24.028662731799127
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available. While dropout proves to be an effective antidote by randomly dropping a proportion of units, existing research has not examined its effect on the self-attention mechanism. In this paper, we investigate this problem through self-attention attribution and find that dropping attention positions with low attribution scores can accelerate training and increase the risk of overfitting. Motivated by this observation, we propose Attribution-Driven Dropout (AD-DROP), which randomly discards some high-attribution positions to encourage the model to make predictions by relying more on low-attribution positions to reduce overfitting. We also develop a cross-tuning strategy to alternate fine-tuning and AD-DROP to avoid dropping high-attribution positions excessively. Extensive experiments on various benchmarks show that AD-DROP yields consistent improvements over baselines. Analysis further confirms that AD-DROP serves as a strategic regularizer to prevent overfitting during fine-tuning.

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