Original: AD-D ROP : Attribution-Driven Dropout for Robust Language Model Fine-Tuning
Revised: AD-D ROP : Attribution-Driven Dropout for Robust Language Model Fine-Tuning

Original: Abstract
Revised: Abstract

Original: Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available.
Revised: Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available.

Original: 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.
Revised: 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.

Original: 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.
Revised: 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.

Original: Motivated by this observation, we propose AttributionDriven Dropout (AD-D ROP ), which randomly discards some high-attribution positions to encourage the model to make predictions by relying more on lowattribution positions to reduce overfitting.
Revised: Motivated by this observation, we propose AttributionDriven Dropout (AD-D ROP ), which randomly discards some high-attribution positions to encourage the model to make predictions by relying more on lowattribution positions to reduce overfitting.

Original: We also develop a cross-tuning strategy to alternate fine-tuning and AD-D ROP to avoid dropping high-attribution positions excessively.
Revised: We also develop a cross-tuning strategy to alternate fine-tuning and AD-D ROP to avoid dropping high-attribution positions excessively.

Original: Extensive experiments on various benchmarks show that AD-D ROP yields consistent improvements over baselines.
Revised: Extensive experiments on various benchmarks show that AD-D ROP yields consistent improvements over baselines.

Original: Analysis further confirms that
Revised: Analysis further confirms that

Original: AD-D ROP serves as a strategic regularizer to prevent overfitting during fine-tuning.
Revised: AD-D ROP serves as a strategic regularizer to prevent overfitting during fine-tuning.

Original:  Introduction
Revised:  Introduction

Original: Pre-training large language models (PrLMs) on massive unlabeled corpora and fine-tuning them on downstream tasks has become a new paradigm [1–3].
Revised: Pre-training large language models (PrLMs) on massive unlabeled corpora and fine-tuning them on downstream tasks has become a new paradigm [1–3].

Original: Their success can be partly attributed to the self-attention mechanism [4], yet these self-attention networks are often redundant [5, 6] and tend to cause overfitting when fine-tuned on downstream tasks due to the mismatch between their overparameterization and the limited annotated data [7–13].
Revised: Their success can be partly attributed to the self-attention mechanism [4], yet these self-attention networks are often redundant [5, 6] and tend to cause overfitting when fine-tuned on downstream tasks due to the mismatch between their overparameterization and the limited annotated data [7–13].

Original: To address this issue, various regularization techniques such as data augmentation [14, 15], adversarial training [16, 17]), and dropout-based methods [11, 13, 18] have been developed.
Revised: To address this issue, various regularization techniques such as data augmentation [14, 15], adversarial training [16, 17]), and dropout-based methods [11, 13, 18] have been developed.

Original: Among them, dropout-based methods are widely adopted for their simplicity and effectiveness.
Revised: Among them, dropout-based methods are widely adopted for their simplicity and effectiveness.

Original: Dropout [19], which randomly discards a proportion of units, is at the core of dropout-based methods.
Revised: Dropout [19], which randomly discards a proportion of units, is at the core of dropout-based methods.

Original: Recently, several variants of dropout have been proposed, such as Concrete Dropout [20], DropBlock [21], and AutoDropout [22].
Revised: Recently, several variants of dropout have been proposed, such as Concrete Dropout [20], DropBlock [21], and AutoDropout [22].

Original: However, these variants generally follow the vanilla dropout to randomly drop units during training and pay little attention to the effect of dropout on self-attention.
Revised: However, these variants generally follow the vanilla dropout to randomly drop units during training and pay little attention to the effect of dropout on self-attention.

Original: In this paper, we seek to fill this gap from the perspective of self-attention attribution [23] and aim to reduce overfitting when fine-tuning PrLMs.
Revised: In this paper, we seek to fill this gap from the perspective of self-attention attribution [23] and aim to reduce overfitting when fine-tuning PrLMs.

Original: Attribution [24] is an interpretability method that attributes model predictions to input features via saliency measures such as gradient [25, 26].
Revised: Attribution [24] is an interpretability method that attributes model predictions to input features via saliency measures such as gradient [25, 26].

Original: It is also used to explain the influence patterns of self-attention in recent literature [23, 27, 28].
Revised: It is also used to explain the influence patterns of self-attention in recent literature [23, 27, 28].

Original: Our prior experiment of self-attention attribution (Section 2.2) reveals that attention positions are not equally important in preventing overfitting, and dropping low-attribution positions is more likely to cause overfitting than discarding high-attribution positions.
Revised: Our prior experiment of self-attention attribution (Section 2.2) reveals that attention positions are not equally important in preventing overfitting, and dropping low-attribution positions is more likely to cause overfitting than discarding high-attribution positions.

Original: This observation suggests that attention positions should not be treated the same in dropout.
Revised: This observation suggests that attention positions should not be treated the same in dropout.

Original: [Figure]
Revised: [Figure]

Original: Unlike vanilla dropout which randomly discards attention positions, AD-D ROP focuses on dropping highattribution positions in candidate discard regions.
Revised: Unlike vanilla dropout which randomly discards attention positions, AD-D ROP focuses on dropping highattribution positions in candidate discard regions.

Original: Motivated by the above, we propose
Revised: Motivated by the above, we propose

Original: A ttribution- D riven Drop out (AD-D ROP ) to better fine-tune PrLMs based on selfattention attribution.
Revised: A ttribution- D riven Drop out (AD-D ROP ) to better fine-tune PrLMs based on selfattention attribution.

Original: The general idea of ADD ROP is to drop a set of self-attention positions with high attribution scores.
Revised: The general idea of ADD ROP is to drop a set of self-attention positions with high attribution scores.

Original: We illustrate the difference between vanilla dropout and AD-D ROP by their attention maps in Figure 1.
Revised: We illustrate the difference between vanilla dropout and AD-D ROP by their attention maps in Figure 1.

Original: When fine-tuning a PrLM on a batch of training samples, AD-D ROP involves four steps.
Revised: When fine-tuning a PrLM on a batch of training samples, AD-D ROP involves four steps.

Original: First, predictions are made through a forward computation without dropping any attention position.
Revised: First, predictions are made through a forward computation without dropping any attention position.

Original: Second, we compute the attribution score of each attention position by gradient [25] or integrated gradient [26] attribution methods.
Revised: Second, we compute the attribution score of each attention position by gradient [25] or integrated gradient [26] attribution methods.

Original: Third, we sample a set of positions with high attribution scores and generate a mask matrix for each attention map.
Revised: Third, we sample a set of positions with high attribution scores and generate a mask matrix for each attention map.

Original: Finally, the mask matrices are applied to the next forward computation to make predictions for backpropagation.
Revised: Finally, the mask matrices are applied to the next forward computation to make predictions for backpropagation.

Original: AD-D ROP can be regarded as a strategic dropout regularizer that forces the model to make predictions by relying more on low-attribution positions to reduce overfitting.
Revised: AD-D ROP can be regarded as a strategic dropout regularizer that forces the model to make predictions by relying more on low-attribution positions to reduce overfitting.

Original: Nevertheless, excessive neglect of high-attribution positions would leave insufficient information for training.
Revised: Nevertheless, excessive neglect of high-attribution positions would leave insufficient information for training.

Original: Hence, we further propose a cross-tuning strategy that performs fine-tuning and AD-D ROP alternately to improve the training stability.
Revised: Hence, we further propose a cross-tuning strategy that performs fine-tuning and AD-D ROP alternately to improve the training stability.

Original: To verify the effectiveness of AD-D ROP , we conduct extensive experiments with different PrLMs (i.e., BERT [1], RoBERTa [2], ELECTRA [29], and OPUS-MT [30]) on various datasets (i.e., GLUE [31], CoNLL-2003 [32], WMT 2016 EN-RO and TR-EN [33], HANS [34], and PAWS-X [35]).
Revised: To verify the effectiveness of AD-D ROP , we conduct extensive experiments with different PrLMs (i.e., BERT [1], RoBERTa [2], ELECTRA [29], and OPUS-MT [30]) on various datasets (i.e., GLUE [31], CoNLL-2003 [32], WMT 2016 EN-RO and TR-EN [33], HANS [34], and PAWS-X [35]).

Original: Experimental results show that the models tuned with AD-D ROP obtain remarkable improvements over that tuned with the original fine-tuning approach.
Revised: Experimental results show that the models tuned with AD-D ROP obtain remarkable improvements over that tuned with the original fine-tuning approach.

Original: For example, on the GLUE benchmark, BERT achieves an average improvement of 1.98/0.87 points on the dev/test sets while RoBERTa achieves an average improvement of 1.29/0.62 points.
Revised: For example, on the GLUE benchmark, BERT achieves an average improvement of 1.98/0.87 points on the dev/test sets while RoBERTa achieves an average improvement of 1.29/0.62 points.

Original: Moreover, ablation studies and analysis demonstrate that gradient-based attribution [25, 26] is a more suitable saliency measure for implementing AD-D ROP than directly using attention weights or simple random sampling.
Revised: Moreover, ablation studies and analysis demonstrate that gradient-based attribution [25, 26] is a more suitable saliency measure for implementing AD-D ROP than directly using attention weights or simple random sampling.

Original: Moreover, they also demonstrate that the cross-tuning strategy plays a crucial role in improving training stability.
Revised: Moreover, they also demonstrate that the cross-tuning strategy plays a crucial role in improving training stability.

Original: To sum up, this work reveals that self-attention positions are not equally important for dropout when fine-tuning PrLMs.
Revised: To sum up, this work reveals that self-attention positions are not equally important for dropout when fine-tuning PrLMs.

Original: Arguably, low-attribution positions are more difficult to optimize than high-attribution positions, and dropping these positions tends not to relieve but accelerate overfitting.
Revised: Arguably, low-attribution positions are more difficult to optimize than high-attribution positions, and dropping these positions tends not to relieve but accelerate overfitting.

Original: This leads to a novel dropout regularizer, AD-D ROP , driven by self-attention attribution.
Revised: This leads to a novel dropout regularizer, AD-D ROP , driven by self-attention attribution.

Original: Although proposed for self-attention units, AD-D ROP can be potentially extended to other units as dropout.
Revised: Although proposed for self-attention units, AD-D ROP can be potentially extended to other units as dropout.

Original:  Methodology 2.1 Preliminaries
Revised:  Methodology 2.1 Preliminaries

Original: Since Transformers [4] are the backbone of PrLMs, we first review the details of self-attention in
Revised: Since Transformers [4] are the backbone of PrLMs, we first review the details of self-attention in

Original: Transformers and self-attention attribution [23].
Revised: Transformers and self-attention attribution [23].

Original: Let X ∈ R n × d be the input of a Transformer block, where n is the sequence length and d is the embedding size.
Revised: Let X ∈ R n × d be the input of a Transformer block, where n is the sequence length and d is the embedding size.

Original: Self-attention in this block first maps X into three matrices Q h , K h and V h via linear projections as query, key, and value respectively for the h -th head.
Revised: Self-attention in this block first maps X into three matrices Q h , K h and V h via linear projections as query, key, and value respectively for the h -th head.

Original: Then, the attention output of this head is calculated as:
Revised: Then, the attention output of this head is calculated as:

Original: [Equation]
Revised: [Equation]

Original: where √ d k is a scaling factor.
Revised: where √ d k is a scaling factor.

Original: M h is the mask matrix to apply dropout in self-attention, and elements in M h will be −∞ if the corresponding positions in attention maps are masked and 0 otherwise.
Revised: M h is the mask matrix to apply dropout in self-attention, and elements in M h will be −∞ if the corresponding positions in attention maps are masked and 0 otherwise.

Original: Based on the attention maps A =
Revised: Based on the attention maps A =

Original: [ A 1 , A 2 , · · · , A H ] for H attention heads, gradient attribution [25, 36] directly produces an attribution matrix B h by computing the following partial derivative:
Revised: [ A 1 , A 2 , · · · , A H ] for H attention heads, gradient attribution [25, 36] directly produces an attribution matrix B h by computing the following partial derivative:

Original: [Equation]
Revised: [Equation]

Original: where F c ( · ) denotes the logit output of the Transformer for class c .
Revised: where F c ( · ) denotes the logit output of the Transformer for class c .

Original: To provide a theoretically more sound attribution method, Sundararajan et al.
Revised: To provide a theoretically more sound attribution method, Sundararajan et al.

Original: [26] propose integrated gradient, which is employed by Hao et al.
Revised: [26] propose integrated gradient, which is employed by Hao et al.

Original: [23] as a saliency measure for self-attention attribution.
Revised: [23] as a saliency measure for self-attention attribution.

Original: Specifically, Hao et al.
Revised: Specifically, Hao et al.

Original: [23] compute the attribution matrix B h as:
Revised: [23] compute the attribution matrix B h as:

Original: [Equation]
Revised: [Equation]

Original: where m is the number of steps for approximating the integration in integrated gradient, and ⊙ is the element-wise multiplication operator.
Revised: where m is the number of steps for approximating the integration in integrated gradient, and ⊙ is the element-wise multiplication operator.

Original: Despite its theoretical advantage over gradient attribution, integrated gradient requires m times more computational effort, which is especially expensive when it is applied to all the attention heads in Transformers.
Revised: Despite its theoretical advantage over gradient attribution, integrated gradient requires m times more computational effort, which is especially expensive when it is applied to all the attention heads in Transformers.

Original: Moreover, our experiments in Section 3.4 show that gradient attribution achieves comparable performance with integrated gradient but requires much less computational cost, suggesting that gradient attribution is more desirable for AD-D ROP .
Revised: Moreover, our experiments in Section 3.4 show that gradient attribution achieves comparable performance with integrated gradient but requires much less computational cost, suggesting that gradient attribution is more desirable for AD-D ROP .

Original: 2.2 A Prior Attribution Experiment
Revised: 2.2 A Prior Attribution Experiment

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original:     
Revised:     

Original: [Equation]
Revised: [Equation]

Original:       
Revised:       

Original: [Figure]
Revised: [Figure]

Original: The dropping rate is set to 0.3 if it applies.
Revised: The dropping rate is set to 0.3 if it applies.

Original: To better motivate our work, we first conduct a prior experiment on MRPC [37] to investigate how different positions in self-attention maps affect fine-tuning performance based on attribution results.
Revised: To better motivate our work, we first conduct a prior experiment on MRPC [37] to investigate how different positions in self-attention maps affect fine-tuning performance based on attribution results.

Original: RoBERTa_base [2] is used as the base model.
Revised: RoBERTa_base [2] is used as the base model.

Original: To begin with, we first perform a forward computation of the model on each batch of training samples to obtain the logit output of each sample corresponding to the gold label.
Revised: To begin with, we first perform a forward computation of the model on each batch of training samples to obtain the logit output of each sample corresponding to the gold label.

Original: Then, we obtain an attribution matrix B h for the selfattention positions in the first layerby gradient attribution with Eq.
Revised: Then, we obtain an attribution matrix B h for the selfattention positions in the first layerby gradient attribution with Eq.

Original: (2) and sort each row of the matrix.
Revised: (2) and sort each row of the matrix.

Original: Finally, we sample a set of self-attention positions with high or low attribution scores in each row to generate a mask matrix M h , which is fed into Eq.
Revised: Finally, we sample a set of self-attention positions with high or low attribution scores in each row to generate a mask matrix M h , which is fed into Eq.

Original: (1) to make the final predictions.
Revised: (1) to make the final predictions.

Original: After each epoch of training, we evaluate the model on the development set.
Revised: After each epoch of training, we evaluate the model on the development set.

Original: Two baseline dropping strategies (i.e., dropping by random sampling and without dropping any position) are employed for comparison.
Revised: Two baseline dropping strategies (i.e., dropping by random sampling and without dropping any position) are employed for comparison.

Original: We plot the loss curves of the model with these dropping strategies on both training and development sets in Figure 2.
Revised: We plot the loss curves of the model with these dropping strategies on both training and development sets in Figure 2.

Original: The observations are threefold.
Revised: The observations are threefold.

Original: First, dropping lowattribution positions makes the model fit the training data rapidly, whereas it performs poorly on the development set, indicating that the model is not properly trained.
Revised: First, dropping lowattribution positions makes the model fit the training data rapidly, whereas it performs poorly on the development set, indicating that the model is not properly trained.

Original: Second, compared with the other dropping strategies, dropping high-attribution positions reduces the fitting speed significantly.
Revised: Second, compared with the other dropping strategies, dropping high-attribution positions reduces the fitting speed significantly.

Original: Third, random dropping only slightly reduces overfitting, compared to the training without dropping.
Revised: Third, random dropping only slightly reduces overfitting, compared to the training without dropping.

Original: These observations suggest that attention positions are of different importance in preventing overfitting.
Revised: These observations suggest that attention positions are of different importance in preventing overfitting.

Original: We conjecture that low-attribution positions are more difficult to optimize than high-attribution positions.
Revised: We conjecture that low-attribution positions are more difficult to optimize than high-attribution positions.

Original: While dropping low-attribution positions tends to accelerate overfitting, discarding high-attribution positions helps reduce overfitting.
Revised: While dropping low-attribution positions tends to accelerate overfitting, discarding high-attribution positions helps reduce overfitting.

Original: 2.3 Attribution-Driven Dropout
Revised: 2.3 Attribution-Driven Dropout

Original: Inspired by the observations in Section 2.2, we propose a novel regularizer, AD-D ROP , to better prevent overfitting when adapting PrLMs to downstream tasks.
Revised: Inspired by the observations in Section 2.2, we propose a novel regularizer, AD-D ROP , to better prevent overfitting when adapting PrLMs to downstream tasks.

Original: The motivation of AD-D ROP is to minimize the over-reliance of these models on particular features which may affect their generalization.
Revised: The motivation of AD-D ROP is to minimize the over-reliance of these models on particular features which may affect their generalization.

Original: Formally, given a training set D = { ( x i , y i ) } Ni =1 of N samples, where x i is the i -th sample and y i is its label, the goal of AD-D ROP is to fine-tune a PrLM F ( · ) of L layers on D .
Revised: Formally, given a training set D = { ( x i , y i ) } Ni =1 of N samples, where x i is the i -th sample and y i is its label, the goal of AD-D ROP is to fine-tune a PrLM F ( · ) of L layers on D .

Original: Same as the vanilla dropout [19], AD-D ROP is only applied in the training phase.
Revised: Same as the vanilla dropout [19], AD-D ROP is only applied in the training phase.

Original: [Figure]
Revised: [Figure]

Original: As shown in Figure 3, the idea of AD-D ROP can be described in four steps.
Revised: As shown in Figure 3, the idea of AD-D ROP can be described in four steps.

Original: First, we conduct a forward computation of the model to obtain the label with the highest probability as the pseudo label.
Revised: First, we conduct a forward computation of the model to obtain the label with the highest probability as the pseudo label.

Original: The reason we adopt pseudo labels rather than gold labels for attribution will be explained shortly.
Revised: The reason we adopt pseudo labels rather than gold labels for attribution will be explained shortly.

Original: Specifically, for the input x i with n tokens, we apply F ( · ) to encode it and obtain its pseudo label
Revised: Specifically, for the input x i with n tokens, we apply F ( · ) to encode it and obtain its pseudo label

Original: ˜ c :
Revised: ˜ c :

Original: [Equation]
Revised: [Equation]

Original: where P F ( c | x i ) is the probability of class c for x i .
Revised: where P F ( c | x i ) is the probability of class c for x i .

Original: After the forward computation, we also obtain a set of attention maps A
Revised: After the forward computation, we also obtain a set of attention maps A

Original: =
Revised: =

Original: [ A 1 , A 2 , · · · , A H ] for each layer according to Eq.
Revised: [ A 1 , A 2 , · · · , A H ] for each layer according to Eq.

Original: Second, we compute the attribution matrices B =
Revised: Second, we compute the attribution matrices B =

Original: [ B 1 , B 2 , · · · , B H ] for H heads according to
Revised: [ B 1 , B 2 , · · · , B H ] for H heads according to

Original: Eq.
Revised: Eq.

Original: Specifically, the attribution matrix B h for the h -th head is computed as:
Revised: Specifically, the attribution matrix B h for the h -th head is computed as:

Original: [Equation]
Revised: [Equation]

Original: where F ˜ c ( A ) is the logit output of pseudo label ˜ c before softmax.
Revised: where F ˜ c ( A ) is the logit output of pseudo label ˜ c before softmax.

Original: Third, we generate a mask matrix M h based on B h .
Revised: Third, we generate a mask matrix M h based on B h .

Original: To this end, we first sort each row of B h in ascending order and obtain a sorted attribution matrix (cid:98) B h .
Revised: To this end, we first sort each row of B h in ascending order and obtain a sorted attribution matrix (cid:98) B h .

Original: Then, we define a candidate discard region
Revised: Then, we define a candidate discard region

Original: S h , in which each element s i,j is defined as:
Revised: S h , in which each element s i,j is defined as:

Original: [Equation]
Revised: [Equation]

Original: where b i,j and (cid:98) b i,j are elements of B h and (cid:98) B h , respectively, int( · ) is an integer function, and p ∈ (0 , 1) is used to control the size of the candidate discard region.
Revised: where b i,j and (cid:98) b i,j are elements of B h and (cid:98) B h , respectively, int( · ) is an integer function, and p ∈ (0 , 1) is used to control the size of the candidate discard region.

Original: Next, we apply dropout in the region to produce the mask matrix M h as:
Revised: Next, we apply dropout in the region to produce the mask matrix M h as:

Original: [Equation]
Revised: [Equation]

Original: where u i,j ∼ Bernoulli(1 − q ) is an element of matrix U h ∈ R n × n , and q is the dropout rate.
Revised: where u i,j ∼ Bernoulli(1 − q ) is an element of matrix U h ∈ R n × n , and q is the dropout rate.

Original: Finally, M h is fed into self-attention of Eq.
Revised: Finally, M h is fed into self-attention of Eq.

Original: (1) for the second forward computation, and the final output is used to calculate the loss for backpropagation.
Revised: (1) for the second forward computation, and the final output is used to calculate the loss for backpropagation.

Original: Discussion
Revised: Discussion

Original: The reasons that AD-D ROP uses pseudo labels for attribution are twofold.
Revised: The reasons that AD-D ROP uses pseudo labels for attribution are twofold.

Original: First, adopting gold labels will divulge label information and lead to inconsistency between training and inference.
Revised: First, adopting gold labels will divulge label information and lead to inconsistency between training and inference.

Original: Second, for misclassified samples in the first forward computation, AD-D ROP with gold labels tends to continue to make incorrect predictions because high-attribution attention positions derived from gold labels may be located in low-attribution regions derived from pseudo labels.
Revised: Second, for misclassified samples in the first forward computation, AD-D ROP with gold labels tends to continue to make incorrect predictions because high-attribution attention positions derived from gold labels may be located in low-attribution regions derived from pseudo labels.

Original: Therefore, dropping these positions does not help the model correct wrong predictions, while ADD ROP with pseudo labels urges the model to rely on important features in the current pass and may correct the wrong predictions.
Revised: Therefore, dropping these positions does not help the model correct wrong predictions, while ADD ROP with pseudo labels urges the model to rely on important features in the current pass and may correct the wrong predictions.

Original: The attribution with gold labels will be investigated in Section 3.4.
Revised: The attribution with gold labels will be investigated in Section 3.4.

Original: 2.4 Cross-Tuning Algorithm
Revised: 2.4 Cross-Tuning Algorithm

Original: We further design a cross-tuning algorithm to avoid dropping high-attribution positions excessively when applying AD-D ROP .
Revised: We further design a cross-tuning algorithm to avoid dropping high-attribution positions excessively when applying AD-D ROP .

Original: The idea of cross-tuning is to execute the original fine-tuning and ADD ROP alternatively.
Revised: The idea of cross-tuning is to execute the original fine-tuning and ADD ROP alternatively.

Original: Specifically, it performs the original fine-tuning at odd epochs and AD-D ROP at even epochs.
Revised: Specifically, it performs the original fine-tuning at odd epochs and AD-D ROP at even epochs.

Original: The overall process of cross-tuning is described in Algorithm 1, where Lines 3-5 are the original fine-tuning operations and Lines 7-9 describe the process of AD-D ROP .
Revised: The overall process of cross-tuning is described in Algorithm 1, where Lines 3-5 are the original fine-tuning operations and Lines 7-9 describe the process of AD-D ROP .

Original:  Experiments 3.1 Datasets
Revised:  Experiments 3.1 Datasets

Original: We conduct our main experiments on eight tasks of the GLUE benchmark [31], including SST- [38], MNLI [39], QNLI [40], QQP [41], CoLA [42], STS-B [43], MRPC [37], and RTE [44].
Revised: We conduct our main experiments on eight tasks of the GLUE benchmark [31], including SST- [38], MNLI [39], QNLI [40], QQP [41], CoLA [42], STS-B [43], MRPC [37], and RTE [44].

Original: The evaluation metrics are Matthew’s Corrcoef (Mcc) [45] for CoLA, Pearson Corrcoef (Pcc) [46] for
Revised: The evaluation metrics are Matthew’s Corrcoef (Mcc) [45] for CoLA, Pearson Corrcoef (Pcc) [46] for

Original: STS-B, and Accuracy (Acc) for the others.
Revised: STS-B, and Accuracy (Acc) for the others.

Original: To demonstrate that AD-D ROP applies to token-level tasks as well, we conduct experiments on Named Entity Recognition (CoNLL-2003 [32]) and Machine
Revised: To demonstrate that AD-D ROP applies to token-level tasks as well, we conduct experiments on Named Entity Recognition (CoNLL-2003 [32]) and Machine

Original: Translation (WMT 2016 [33]) datasets, the results of which are shown in Appendix A.2.
Revised: Translation (WMT 2016 [33]) datasets, the results of which are shown in Appendix A.2.

Original: Besides, we also evaluate AD-D ROP on two out-of-distribution (OOD) datasets, including HANS [34] and
Revised: Besides, we also evaluate AD-D ROP on two out-of-distribution (OOD) datasets, including HANS [34] and

Original: PAWS-X [35].
Revised: PAWS-X [35].

Original: The details of these datasets are introduced in Appendix C.1.
Revised: The details of these datasets are introduced in Appendix C.1.

Original: 3.2 Implementation Details
Revised: 3.2 Implementation Details

Original: We implement our AD-D ROP in Pytorch with the Transformers package [47].
Revised: We implement our AD-D ROP in Pytorch with the Transformers package [47].

Original: We train the selected
Revised: We train the selected

Original: PrLMs on GeForce RTX 3090 GPUs.
Revised: PrLMs on GeForce RTX 3090 GPUs.

Original: We tune the learning rate in {1e-5, 2e-5, 3e-5} and the batch size in {16, 32, 64}.
Revised: We tune the learning rate in {1e-5, 2e-5, 3e-5} and the batch size in {16, 32, 64}.

Original: Following Miao et al.
Revised: Following Miao et al.

Original: [17], we perform early stopping to choose the number of training epochs on GLUE.
Revised: [17], we perform early stopping to choose the number of training epochs on GLUE.

Original: The two critical hyperparameters p and q are searched within [0 . 1 , 0 . 9] with step size 0.1.
Revised: The two critical hyperparameters p and q are searched within [0 . 1 , 0 . 9] with step size 0.1.

Original: For integrated gradient in Eq.
Revised: For integrated gradient in Eq.

Original: (3), we follow Hao et al.
Revised: (3), we follow Hao et al.

Original: [23] and set m to 20.
Revised: [23] and set m to 20.

Original: We apply AD-D ROP only in the first layer for the datasets of SST-2, MNLI, QNLI, QQP, and STS-B since the fine-tuning on these datasets is stable and less likely to cause overfitting.
Revised: We apply AD-D ROP only in the first layer for the datasets of SST-2, MNLI, QNLI, QQP, and STS-B since the fine-tuning on these datasets is stable and less likely to cause overfitting.

Original: For the rest datasets, we apply AD-D ROP in all layers.
Revised: For the rest datasets, we apply AD-D ROP in all layers.

Original: We provide the detailed hyperparameter
Revised: We provide the detailed hyperparameter

Original: [Table]
Revised: [Table]

Original: 3.3 Overall Results
Revised: 3.3 Overall Results

Original: We report the overall results of the fine-tuned models in Table 1.
Revised: We report the overall results of the fine-tuned models in Table 1.

Original: We first compare AD-D ROP with existing regularization methods on the development sets, including the original fine-tuning, SCAL [17], SuperT [48], R-Drop [18]
Revised: We first compare AD-D ROP with existing regularization methods on the development sets, including the original fine-tuning, SCAL [17], SuperT [48], R-Drop [18]

Original: [Table]
Revised: [Table]

Original: and HiddenCut [15].
Revised: and HiddenCut [15].

Original: We observe that AD-D ROP surpasses the baselines on most of the datasets.
Revised: We observe that AD-D ROP surpasses the baselines on most of the datasets.

Original: Specifically, AD-D ROP yields an average improvement of 1. and 1.29 points on BERT base and RoBERTa base , respectively.
Revised: Specifically, AD-D ROP yields an average improvement of 1. and 1.29 points on BERT base and RoBERTa base , respectively.

Original: We then discuss the performance of
Revised: We then discuss the performance of

Original: AD-D ROP on the test sets.
Revised: AD-D ROP on the test sets.

Original: Results in Table 1 show that AD-D ROP achieves consistent improvement, boosting the average scores of BERT base and RoBERTa base by 0.87 and 0.62, respectively.
Revised: Results in Table 1 show that AD-D ROP achieves consistent improvement, boosting the average scores of BERT base and RoBERTa base by 0.87 and 0.62, respectively.

Original: Besides, compared with large datasets, AD-D ROP achieves more gains on small datasets, which are more likely to cause overfitting, illustrating that AD-D ROP is more suitable for small data scenarios
Revised: Besides, compared with large datasets, AD-D ROP achieves more gains on small datasets, which are more likely to cause overfitting, illustrating that AD-D ROP is more suitable for small data scenarios

Original: [Table]
Revised: [Table]

Original: 3.4 Ablation Study
Revised: 3.4 Ablation Study

Original: [Table][Table]
Revised: [Table]

Original: We conduct ablation experiments on four small datasets to investigate the impact of different components.
Revised: We conduct ablation experiments on four small datasets to investigate the impact of different components.

Original: Due to the limited number of submissions imposed by the
Revised: Due to the limited number of submissions imposed by the

Original: GLUE server for evaluation, the results here are reported on the development sets.
Revised: GLUE server for evaluation, the results here are reported on the development sets.

Original: Attribution methods
Revised: Attribution methods

Original: AD-D ROP can be implemented with different attribution methods to generate the mask matrix in
Revised: AD-D ROP can be implemented with different attribution methods to generate the mask matrix in

Original: Eq.
Revised: Eq.

Original: (1), such as integrated gradient attribution (IGA) introduced Eq.
Revised: (1), such as integrated gradient attribution (IGA) introduced Eq.

Original: (3), attention weights for attribution (AA), and randomly generating the discard region (RD) in Eq.
Revised: (3), attention weights for attribution (AA), and randomly generating the discard region (RD) in Eq.

Original: We replace the gradient attribution (GA) in Eq.
Revised: We replace the gradient attribution (GA) in Eq.

Original: (5)-(6) with these methods.
Revised: (5)-(6) with these methods.

Original: From Table 2, we can make three observations.
Revised: From Table 2, we can make three observations.

Original: First, AD-D ROP with gradientbased attribution methods (GA and IGA) surpasses that with the other methods (AA or RD) on most of the datasets, illustrating that gradientbased methods are better at finding features that are likely to cause overfitting.
Revised: First, AD-D ROP with gradientbased attribution methods (GA and IGA) surpasses that with the other methods (AA or RD) on most of the datasets, illustrating that gradientbased methods are better at finding features that are likely to cause overfitting.

Original: Second, IGA outperforms GA in some cases.
Revised: Second, IGA outperforms GA in some cases.

Original: Although IGA provides better theoretical justification than GA for attribution, it requires prohibitively more computational cost than GA (see Section 4.7 for efficiency analysis), making GA a more desirable choice for AD-D ROP .
Revised: Although IGA provides better theoretical justification than GA for attribution, it requires prohibitively more computational cost than GA (see Section 4.7 for efficiency analysis), making GA a more desirable choice for AD-D ROP .

Original: Third, AD-D ROP improves the original
Revised: Third, AD-D ROP improves the original

Original: BERT base and RoBERTa base with any of the masking strategies, demonstrating the robustness of
Revised: BERT base and RoBERTa base with any of the masking strategies, demonstrating the robustness of

Original: AD-D ROP to overfitting when fine-tuning these models.
Revised: AD-D ROP to overfitting when fine-tuning these models.

Original: Pseudo labels vs gold labels In Section 2.3, we discuss the motivation of using pseudo labels for attribution in AD-D ROP .
Revised: Pseudo labels vs gold labels In Section 2.3, we discuss the motivation of using pseudo labels for attribution in AD-D ROP .

Original: To verify the reasonability, we conduct an experiment with gold labels for attribution.
Revised: To verify the reasonability, we conduct an experiment with gold labels for attribution.

Original: As the results show in Table 2, using gold labels for attribution deteriorates the performance, illustrating that AD-D ROP with pseudo labels for attribution is preferable.
Revised: As the results show in Table 2, using gold labels for attribution deteriorates the performance, illustrating that AD-D ROP with pseudo labels for attribution is preferable.

Original: [Figure]
Revised: [Figure]

Original: Cross-tuning To verify the effectiveness of the cross-tuning strategy, we ablate it and apply only AD-D ROP in all training epochs.
Revised: Cross-tuning To verify the effectiveness of the cross-tuning strategy, we ablate it and apply only AD-D ROP in all training epochs.

Original: As shown in Table 2, removing cross-tuning causes noticeable performance degradation on most of the datasets.
Revised: As shown in Table 2, removing cross-tuning causes noticeable performance degradation on most of the datasets.

Original: This can be explained by the intuition that AD-D ROP without cross-tuning tends to discard high-attribution positions excessively and make the model difficult to converge normally.
Revised: This can be explained by the intuition that AD-D ROP without cross-tuning tends to discard high-attribution positions excessively and make the model difficult to converge normally.

Original: To vividly demonstrate the effect of ADD ROP with or without cross-tuning, we visualize the distributions of the performance on the
Revised: To vividly demonstrate the effect of ADD ROP with or without cross-tuning, we visualize the distributions of the performance on the

Original: RTE 4 development set when enumerating the parameters p and q in the range of [0.1, 0.9].
Revised: RTE 4 development set when enumerating the parameters p and q in the range of [0.1, 0.9].

Original: The results are plotted in Figure 4, where each blue/orange point denotes the accuracy with a pair of p and q values.
Revised: The results are plotted in Figure 4, where each blue/orange point denotes the accuracy with a pair of p and q values.

Original: We observe from the figure that AD-D ROP without cross-tuning cannot be trained properly under some parameter settings.
Revised: We observe from the figure that AD-D ROP without cross-tuning cannot be trained properly under some parameter settings.

Original: However, it works well for most parameter settings when cross-tuning is applied, demonstrating that cross-tuning is vital for improving training stability.
Revised: However, it works well for most parameter settings when cross-tuning is applied, demonstrating that cross-tuning is vital for improving training stability.

Original:  Analysis
Revised:  Analysis

Original: In this section, we further conduct several experiments for more thorough analysis.
Revised: In this section, we further conduct several experiments for more thorough analysis.

Original: 4.1 Repeated Experiments
Revised: 4.1 Repeated Experiments

Original: [Table]
Revised: [Table]

Original: To reduce the influence of randomness, we conduct repeated experiments on four small datasets (i.e.,
Revised: To reduce the influence of randomness, we conduct repeated experiments on four small datasets (i.e.,

Original: CoLA, STS-B, MRPC and RTE).
Revised: CoLA, STS-B, MRPC and RTE).

Original: We repeat the training of each model with five random seeds and report the average score and standard deviation on the development sets.
Revised: We repeat the training of each model with five random seeds and report the average score and standard deviation on the development sets.

Original: From Table 3, we observe that AD-D ROP outperforms the original fine-tuning on all the datasets.
Revised: From Table 3, we observe that AD-D ROP outperforms the original fine-tuning on all the datasets.

Original: In addition, AD-D ROP results in lower standard deviations on most of the datasets, showing that AD-D ROP is more robust in fine-tuning PrLMs than the original approach.
Revised: In addition, AD-D ROP results in lower standard deviations on most of the datasets, showing that AD-D ROP is more robust in fine-tuning PrLMs than the original approach.

Original: 4.2 Effect of Data Size
Revised: 4.2 Effect of Data Size

Original: To study the impact of data size, we compare AD-D ROP with the original fine-tuning (FT) approach on QNLI and QQP, 5 two relatively large datasets, and report their performance when the number of training samples changes.
Revised: To study the impact of data size, we compare AD-D ROP with the original fine-tuning (FT) approach on QNLI and QQP, 5 two relatively large datasets, and report their performance when the number of training samples changes.

Original: RoBERTa is chosen as the base model.
Revised: RoBERTa is chosen as the base model.

Original: Figure 5 shows that AD-D ROP
Revised: Figure 5 shows that AD-D ROP

Original: Results
Revised: Results

Original: [Figure]
Revised: [Figure]

Original:    
Revised:    

Original: [Figure]
Revised: [Figure]

Original:  
Revised: 

Original:    
Revised:      

Original:     
Revised: 

Original: [Figure] outperforms FT consistently on QNLI.
Revised: [Figure] outperforms FT consistently on QNLI.

Original: Moreover, ADD ROP improves the efficiency of data use as training ADD ROP with 60% training samples produces comparable performance to FT with full data.
Revised: Moreover, ADD ROP improves the efficiency of data use as training ADD ROP with 60% training samples produces comparable performance to FT with full data.

Original: 4.3 Hyperparameter Sensitivity
Revised: 4.3 Hyperparameter Sensitivity

Original: AD-D ROP involves two hyperparameters p and q to control the number of discarded attention positions.
Revised: AD-D ROP involves two hyperparameters p and q to control the number of discarded attention positions.

Original: To investigate the sensitivity of AD-D ROP to them, we show the results of different p and q combinations on CoLA and
Revised: To investigate the sensitivity of AD-D ROP to them, we show the results of different p and q combinations on CoLA and

Original: RTE in Figure 6, in which we apply MaxAbsScaler 6 to project the difference between the results of AD-D ROP and FT into the interval of [ − 1 . 0 , 1 . 0] .
Revised: RTE in Figure 6, in which we apply MaxAbsScaler 6 to project the difference between the results of AD-D ROP and FT into the interval of [ − 1 . 0 , 1 . 0] .

Original: We observe that
Revised: We observe that

Original: BERT with AD-D ROP is not hyperparameter-sensitive as it outperforms the baseline under most settings.
Revised: BERT with AD-D ROP is not hyperparameter-sensitive as it outperforms the baseline under most settings.

Original: In contrast, RoBERTa with AD-D ROP is more sensitive and requires a careful search for optimal hyperparameter settings.
Revised: In contrast, RoBERTa with AD-D ROP is more sensitive and requires a careful search for optimal hyperparameter settings.

Original: The possible reason is that RoBERTa is pre-trained with more data and more effective tasks than BERT, making it less prone to overfitting than BERT.
Revised: The possible reason is that RoBERTa is pre-trained with more data and more effective tasks than BERT, making it less prone to overfitting than BERT.

Original: 
Revised: 

Original:      
Revised:      

Original:               
Revised:         

Original: p q (a) BERT on CoLA 
Revised:       

Original:        
Revised: p q (a) BERT on CoLA          

Original:         
Revised:      

Original:       
Revised:       

Original: p q ([Figure]
Revised: p q (

Original: RoBERTa on CoLA 
Revised: [Figure]

Original:        
Revised: RoBERTa on CoLA          

Original:         
Revised:      

Original:       
Revised:       

Original: p q (
Revised: p q (

Original: [Figure]
Revised: [Figure]

Original: BERT[Figure]
Revised: BERT

Original: RTE       
Revised: [Figure]

Original:     
Revised: RTE                

Original:             
Revised:       

Original: p q (
Revised: p q (

Original: [Figure]
Revised: [Figure]

Original: RoBERTa on RTE
Revised: RoBERTa on RTE

Original: 4.4 Larger Model Size
Revised: 4.4 Larger Model Size

Original: [Table]
Revised: [Table]

Original: To verify the scalability of AD-D ROP for a larger model size, we evaluate AD-D ROP withRoBERTa large on the RTE and MRPC datasets.
Revised: To verify the scalability of AD-D ROP for a larger model size, we evaluate AD-D ROP with

Original:  Table 4 shows the average scores and standard deviations of five random seeds. 
Revised: RoBERTa large on the RTE and MRPC datasets. Table 4 shows the average scores and standard deviations of five random seeds. There are two main observations.

Original: There are two main observations. First, AD-D ROP achieves consistent improvements over the larger RoBERTa model, illustrating that AD-D ROP is scalable to large models.
Revised: First, AD-D ROP achieves consistent improvements over the larger RoBERTa model, illustrating that AD-D ROP is scalable to large models.

Original: Second, compared with RoBERTa base on RTE in Table 3, the larger model significantly reduces the deviation (from 1.7 to 0.86), suggesting that a larger model size indeed helps to improve the stability.
Revised: Second, compared with RoBERTa base on RTE in Table 3, the larger model significantly reduces the deviation (from 1.7 to 0.86), suggesting that a larger model size indeed helps to improve the stability.

Original: AD-D ROP further improves the performance and reduces the deviation.
Revised: AD-D ROP further improves the performance and reduces the deviation.

Original: 4.5 Few-shot Scenario
Revised: 4.5 Few-shot Scenario

Original: In this subsection, we test the performance of AD-D ROP under few-shot scenarios.
Revised: In this subsection, we test the performance of AD-D ROP under few-shot scenarios.

Original: Specifically, we carry out 16-, 64-, and 256-shot experiments on SST-2 and CoLA with RoBERTa base as the base model and the baseline.
Revised: Specifically, we carry out 16-, 64-, and 256-shot experiments on SST-2 and CoLA with RoBERTa base as the base model and the baseline.

Original: We report the average scores and standard deviations of five random seeds in
Revised: We report the average scores and standard deviations of five random seeds in

Original: Table 5.
Revised: Table 5.

Original: We observe that RoBERTa with AD-D ROP consistently outperforms the original fine-tuning approach.
Revised: We observe that RoBERTa with AD-D ROP consistently outperforms the original fine-tuning approach.

Original: Besides, AD-D ROP tends to bring more benefits when fewer samples are available.
Revised: Besides, AD-D ROP tends to bring more benefits when fewer samples are available.

Original: [Table]
Revised: [Table]

Original: 4.6 Out-of-Distribution Generalization
Revised: 4.6 Out-of-Distribution Generalization

Original: [Table]
Revised: [Table]

Original: To further demonstrate AD-D ROP is beneficial to reducing overfitting, we test AD-D ROP with
Revised: To further demonstrate AD-D ROP is beneficial to reducing overfitting, we test AD-D ROP with

Original: RoBERTa base on two out-of-distribution (OOD) datasets, i.e., HANS and PAWS-X. For HANS, we use the checkpoints trained on MNLI and test their performance on the validation set (the test set is not supplied).
Revised: RoBERTa base on two out-of-distribution (OOD) datasets, i.e., HANS and PAWS-X. For HANS, we use the checkpoints trained on MNLI and test their performance on the validation set (the test set is not supplied).

Original: For PAWS-X, we use the checkpoints trained on QQP and examine its performance on the test set.
Revised: For PAWS-X, we use the checkpoints trained on QQP and examine its performance on the test set.

Original: The evaluation metric is accuracy.
Revised: The evaluation metric is accuracy.

Original: From
Revised: From

Original: Table 6, we can see that RoBERTa with AD-D ROP achieves better generalization, where AD-D ROP boosts the performance by 0.66 on HANS and 3.35 on PAWS-X, illustrating that the model trained with AD-D ROP generalizes better to OOD data.
Revised: Table 6, we can see that RoBERTa with AD-D ROP achieves better generalization, where AD-D ROP boosts the performance by 0.66 on HANS and 3.35 on PAWS-X, illustrating that the model trained with AD-D ROP generalizes better to OOD data.

Original: 4.7 Computational Efficiency
Revised: 4.7 Computational Efficiency

Original: To analyze the computational efficiency, we quantitatively study the computational cost of AD-D ROP with different dropping strategies (GA, IGA, AA, and RD) relative to the original fine-tuning on
Revised: To analyze the computational efficiency, we quantitatively study the computational cost of AD-D ROP with different dropping strategies (GA, IGA, AA, and RD) relative to the original fine-tuning on

Original: CoLA, STS-B, MRPC, and RTE.
Revised: CoLA, STS-B, MRPC, and RTE.

Original: BERT is chosen as the base model for this experiment.
Revised: BERT is chosen as the base model for this experiment.

Original: As shown in Table 7, although IGA achieves more favorable performance on one of the datasets, it requires higher computational costs than its counterparts, especially when applied in all the layers.
Revised: As shown in Table 7, although IGA achieves more favorable performance on one of the datasets, it requires higher computational costs than its counterparts, especially when applied in all the layers.

Original: In contrast,
Revised: In contrast,

Original: AD-D ROP with GA is more competitive in both performance and computational cost
Revised: AD-D ROP with GA is more competitive in both performance and computational cost

Original: [Table]
Revised: [Table]

Original: [Table]
Revised: [Table]

Original:  Related Work
Revised:  Related Work

Original: Dropout Dropout is a widely used regularizer to mitigate overfitting when training deep neural networks.
Revised: Dropout Dropout is a widely used regularizer to mitigate overfitting when training deep neural networks.

Original: Vanilla dropout [19] randomly selects neurons with a predefined probability and sets their values to zeros during training.
Revised: Vanilla dropout [19] randomly selects neurons with a predefined probability and sets their values to zeros during training.

Original: By doing so, the neurons cannot co-adapt and the trained networks can lead to better generalization.
Revised: By doing so, the neurons cannot co-adapt and the trained networks can lead to better generalization.

Original: In recent years, many variants of dropout have emerged.
Revised: In recent years, many variants of dropout have emerged.

Original: One body of research aims to adopt different strategies to drop units in neural networks.
Revised: One body of research aims to adopt different strategies to drop units in neural networks.

Original: For example,
Revised: For example,

Original: DropConnect [49] randomly selects connections between neurons to discard.
Revised: DropConnect [49] randomly selects connections between neurons to discard.

Original: DropBlock [21] defines a structured dropout that randomly drops the units in a specific contiguous region of a feature map. AutoDropout [22] aims to improve the dropout pattern of DropBlock by introducing an automatic method to design dropout structures.
Revised: DropBlock [21] defines a structured dropout that randomly drops the units in a specific contiguous region of a feature map. AutoDropout [22] aims to improve the dropout pattern of DropBlock by introducing an automatic method to design dropout structures.

Original: HiddenCut [15] drops contiguous spans within the hidden space, in which the attention weights are utilized to select the dropped spans strategically.
Revised: HiddenCut [15] drops contiguous spans within the hidden space, in which the attention weights are utilized to select the dropped spans strategically.

Original: Another body of research devotes to addressing the inconsistency between training and inference when dropout is applied.
Revised: Another body of research devotes to addressing the inconsistency between training and inference when dropout is applied.

Original: For instance, mixout [11] randomly replaces selected parameters with original pre-trained weights rather than setting them to zeros.
Revised: For instance, mixout [11] randomly replaces selected parameters with original pre-trained weights rather than setting them to zeros.

Original: CHILD-TUNING [13] selects a child network and masks out the gradients of the non-child network during the backward step, only updating weights in the child network.
Revised: CHILD-TUNING [13] selects a child network and masks out the gradients of the non-child network during the backward step, only updating weights in the child network.

Original: R-Drop [18] performs dropout twice in the forward steps to produce two sub-models and then applies KL-divergence for their output distributions, forcing the two sub-models to be consistent with each other.
Revised: R-Drop [18] performs dropout twice in the forward steps to produce two sub-models and then applies KL-divergence for their output distributions, forcing the two sub-models to be consistent with each other.

Original: However, most of these methods follow the random sampling strategy of dropout and pay little attention to the different importance of self-attention positions in PrLMs.
Revised: However, most of these methods follow the random sampling strategy of dropout and pay little attention to the different importance of self-attention positions in PrLMs.

Original: Attribution Numerous studies have been undertaken to interpret the behaviors of deep neural networks (DNNs).
Revised: Attribution Numerous studies have been undertaken to interpret the behaviors of deep neural networks (DNNs).

Original: As a theory for interpretability, attribution aims to evaluate the impact of input features on predictions [24].
Revised: As a theory for interpretability, attribution aims to evaluate the impact of input features on predictions [24].

Original: Generally, attribution methods can be divided into perturbation-based [50–52], gradient-related [25, 53, 26], and attention-based [54–56] methods.
Revised: Generally, attribution methods can be divided into perturbation-based [50–52], gradient-related [25, 53, 26], and attention-based [54–56] methods.

Original: We focus on reviewing the gradient-related methods as they are more relevant to our work.
Revised: We focus on reviewing the gradient-related methods as they are more relevant to our work.

Original: Specifically, earlier works [25, 57, 58] try to explain model decisions via gradients since gradients indicate the direction and rate that changes the loss the fastest.
Revised: Specifically, earlier works [25, 57, 58] try to explain model decisions via gradients since gradients indicate the direction and rate that changes the loss the fastest.

Original: However, Sundararajan et al.
Revised: However, Sundararajan et al.

Original: [26] point out that gradient attribution violates the sensitivity axiom in some cases that the gradients will be zero for the function in saturated areas, and propose integrated gradient as a theoretically more sound attribution method.
Revised: [26] point out that gradient attribution violates the sensitivity axiom in some cases that the gradients will be zero for the function in saturated areas, and propose integrated gradient as a theoretically more sound attribution method.

Original: Other efforts have been devoted to revealing the behavior patterns of PrLMs.
Revised: Other efforts have been devoted to revealing the behavior patterns of PrLMs.

Original: Kovaleva et al.
Revised: Kovaleva et al.

Original: [8] and
Revised: [8] and

Original: Clark et al.
Revised: Clark et al.

Original: [9] use attention weights for attribution to investigate what specific knowledge BERT [1] learns.
Revised: [9] use attention weights for attribution to investigate what specific knowledge BERT [1] learns.

Original: Jain and Wallace [27] and Brunner et al.
Revised: Jain and Wallace [27] and Brunner et al.

Original: [59] investigate the identifiability of attention weights and conclude that attention weights are not a faithful explanation for model predictions.
Revised: [59] investigate the identifiability of attention weights and conclude that attention weights are not a faithful explanation for model predictions.

Original: Hao et al.
Revised: Hao et al.

Original: [23] apply integrated gradient [26] as a saliency measure for self-attention attribution in BERT, and use the attribution result to interpret information interactions inside Transformers.
Revised: [23] apply integrated gradient [26] as a saliency measure for self-attention attribution in BERT, and use the attribution result to interpret information interactions inside Transformers.

Original: Similarly,
Revised: Similarly,

Original: Lu et al.
Revised: Lu et al.

Original: [28] develop influence patterns based on integrated gradient to explain information flow in BERT.
Revised: [28] develop influence patterns based on integrated gradient to explain information flow in BERT.

Original: Unlike these works, we aim to examine the effect of dropout on self-attention through self-attention attribution and to reduce overfitting when fine-tuning PrLMs.
Revised: Unlike these works, we aim to examine the effect of dropout on self-attention through self-attention attribution and to reduce overfitting when fine-tuning PrLMs.

Original:  Conclusion
Revised:  Conclusion

Original: We propose a novel dropout regularizer, AD-D ROP , to mitigate overfitting when fine-tuning PrLMs on downstream tasks.
Revised: We propose a novel dropout regularizer, AD-D ROP , to mitigate overfitting when fine-tuning PrLMs on downstream tasks.

Original: Unlike previous dropout-based methods that generally adopt the random sampling strategy to discard units, AD-D ROP draws inspiration from self-attention attribution which reveals that attention positions are not equally important in reducing overfitting and that dropping inappropriate positions may exacerbate the problem.
Revised: Unlike previous dropout-based methods that generally adopt the random sampling strategy to discard units, AD-D ROP draws inspiration from self-attention attribution which reveals that attention positions are not equally important in reducing overfitting and that dropping inappropriate positions may exacerbate the problem.

Original: Therefore, AD-D ROP focuses on discarding high-attribution attention positions to prevent the model from relying heavily on these positions to make predictions.
Revised: Therefore, AD-D ROP focuses on discarding high-attribution attention positions to prevent the model from relying heavily on these positions to make predictions.

Original: Besides, we propose a cross-tuning strategy that performs the original fine-tuning and our AD-D ROP alternately to stabilize the fine-tuning process.
Revised: Besides, we propose a cross-tuning strategy that performs the original fine-tuning and our AD-D ROP alternately to stabilize the fine-tuning process.

Original: Extensive experiments and analysis on the GLUE benchmark demonstrate the effectiveness of AD-D ROP .
Revised: Extensive experiments and analysis on the GLUE benchmark demonstrate the effectiveness of AD-D ROP .

Original: Although originally proposed and evaluated based on self-attention attribution, AD-D ROP can be potentially extended to other neural network units as vanilla dropout, which deserves further research efforts.
Revised: Although originally proposed and evaluated based on self-attention attribution, AD-D ROP can be potentially extended to other neural network units as vanilla dropout, which deserves further research efforts.

Original: Acknowledgments
Revised: Acknowledgments

Original: We appreciate the anonymous reviewers for their valuable comments.
Revised: We appreciate the anonymous reviewers for their valuable comments.

Original: We thank Feifan Yang, Zihong
Revised: We thank Feifan Yang, Zihong

Original: Liang, and Hong Ding for their early discussions.
Revised: Liang, and Hong Ding for their early discussions.

Original: This work was supported by the National Natural
Revised: This work was supported by the National Natural

Original: Science Foundation of China (No. 62176270), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (No. 2017ZT07X355), and the Foundation of Key Laboratory of Machine
Revised: Science Foundation of China (No. 62176270), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (No. 2017ZT07X355), and the Foundation of Key Laboratory of Machine

Original: Intelligence and Advanced Computing of the Ministry of Education.
Revised: Intelligence and Advanced Computing of the Ministry of Education.