How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
Authors: Xinshuai Dong
Anh Tuan Luu
Min Lin
Shuicheng YAN
Hanwang Zhang
Venue: NeurIPS 2021 Poster (2021)
Keywords: Natural Language Processing
Pre-trained Language Models
Adversarial Robustness

Abstract: The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet, it is strikingly vulnerable to adversarial examples, e.g., word substitution attacks using only synonyms can easily fool a BERT-based sentiment analysis model. In this paper, we demonstrate that adversarial training, the prevalent defense technique, does not directly fit a conventional fine-tuning scenario, because it suffers severely from catastrophic forgetting: failing to retain the generic and robust linguistic features that have already been captured by the pre-trained model. In this light, we propose Robust Informative Fine-Tuning (RIFT), a novel adversarial fine-tuning method from an information-theoretical perspective. In particular, RIFT encourages an objective model to retain the features learned from the pre-trained model throughout the entire fine-tuning process, whereas a conventional one only uses the pre-trained weights for initialization. Experimental results show that RIFT consistently outperforms the state-of-the-arts on two popular NLP tasks: sentiment analysis and natural language inference, under different attacks across various pre-trained language models.

How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
Xinshuai Dong Nanyang Technological University & Sea Al Lab Setggiinsuai@outloooo com
Luu Anh Tuan
Nanyang Technological University anhtuan, luu@ntu. edu. sg
Min Lin Sea AI Lab
Shuicheng Yan
Hanwang Zhang
Sea AI Lab yansc&sea. com
Nanyang Technological University hanwangzhangEntu.. edu.
linmin@sea. com
Abstract
The fine-tuning of pre-trained language models has great success in many NLP fields. Yet, is strikingly vulnerable to adversarial examples, e,g., word substitution attacks using only synonyms can easily fool a BERT-based sentiment analysis model. In this paper, we demonstrate that adversarial training, the prevalent defense technique, does not directly fit a conventional fie-tuning scenario, because it suffers severely from catastrophic forgetting: failing to retain the generic and robust linguistic features that have already been captured by the pre-trained model. In this light, we propose Robust Informative Fine-T Tuning (RIFT), novel adversarial fine-tuning method from an inffrmationn-hhoretiici perspective. particular, RIFT encourages an objective model to retain the features learned from the pre-trained model throughout the entire fine-tuning process, whereas a conventional one only uses the pre-trained weights for initialization. Experimental results show that RIFT consistently outperforms the state-of-the-ars on two popular NLP tasks: sentiment analysis and natural language inference, under different attacks across various pre-trained language models.
1
Introduction
Deep models are well-known to vulnerable adversarial examples [64] 19 50 35). For instance,
fine-tuned models pre-trained very large corpora can be easily fooled by word substitution attacks using only synonyms [2] 58
32 [2] This has raised grand security challenges to modern NLP systems, such as spam filtering and malware detection, where pre-trained language models like BERT
[1 are widely deployed.
Attack algorithms 19
aim to maliciously generate adversarial examples to fool victim model, while adversarial defense aims at building robustness against them. Among the defense methods, adversarial training (64
76
40
is the most effective one 33. It updates model parameters using perturbed adversarial samples generated on-the-fly and yields consistently robust performance even against the challenging adaptive attacks 3 68].
19
48 45
However, despite its effectiveness in training from scratch, adversarial training may not directly fit the current NLP paradigm, the fine-tuning of pre-trained language models. First, fine-tuning per se suffers from catastrophic forgetting [46 to small fine-tuning data set, which may deviate too far from the pre-trained knowledge [25] 81]. Second, advessarialy fine-tuned models are more likely forget: adversarial samples are usually
i.e.. the resultant model tends over-fit
Our code will available at http://giittalccoooooggnnnnniiit
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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S: Feature by Objective Model
T: Feature by Pre-trained Model
Objective: Maximizing I(S: Y = I(SYY) Y)
HIS)
H(T)
H(Y)
I(S:T) Y)
Y: Class Label
I(S:)
Figure I: An illustration of the overall objective RIFT. Maximizing [(S; encourages features
of the objective model to be predictive of the class label, while maximizing ((S; 7| Y) encourages learning robust and generic linguistic information from the pre-trained model. (Random variable denotes extracted features of X by the objective model and T by the pre-trained language model)
out-of-distribution 38 63
thus they generally inconsistent with the pre-trained model. As result, adversarial mun-tuning fails to memorize all the robust and generic linguistic features already learned during pre- vee-training [65 57]. which are however very beneficial for robust objective model.
Addressing forgetting essential for achieving more robust objective model. Conventional wisdom such as pre-trained weight decay [8 10 and random mixout (37 mitigates forgetting by constraining 1p distance between the two models' parameters. Though effective to some extent, however, limited because change in the model parameter space only serves an imperfect proxy for that in the function space [5]. Besides, the extent to which an encoder fails to retain information also depends on the input distribution. Therefore, better way to encourage memorization is favorable.
In this paper, we follow an information-theorrtiee lens look into the forgetting problem: employ mutual information to measure how well an objective model memorizes the useful features captured before. This motivates our novel adversarial fine-tuning method, Robust Informative Fine- Tuning (RIFT). In addition to fitting a down-stream task as conventions, RIFT maximizes the mutual information between the output of objective model and that of the pre-trained model conditioned on the class label. It encourages an objective model to continuously retain useful information from the pre-trained one throughout the whole fine-tuning process, whereas the conventional one only makes use of the pre-trained weights for initialization. We illustrate the overall objective of RIFT in Figure and summarize the major contributions this paper as follows:
To the best of our knowledge, we are the first to investigate the intensified phenomenon of catastrophic forgetting in the adversarial fine-tuning of pre-trained language models.
To address the forgetting problem and achieve more robust models, propose novel approach named Robust Informative Fine-Tuning (RIFT) from an information-theoretica. perspective. RIFT enables an objective model retain robust and generic linguistic features throughout the whole fine-tuning process and thus enhance robustness against adversarial examples.
We empirically evaluate RIFT on two prevailing NLP tasks, sentiment analysis and natural language inference, where RIFT consistently outperforms the state-of-the-ard terms of robustness, under different attacks across various pre-trained language models.
2 Methodology
2.1 Notations and Problem Setting
In this paper, we focus on text classification task to introduce our method, while it can easily extended to other NLP tasks. We suppose random variables X, Y pD(x, (p,,,)), where X represents the textual input, represents the class label, pp is the data distribution, and at y are the observed values. Our goal is to build a classifier Fx()), where F.).,, referred to our objective model

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Asserattl firatunng BERT
Standarrd Freduring 2emr
[Wea Weells (109slls
adurecatty fireuing koaserss
5/4
---
Sta card Tieuuing Recetts
5.7%
2100
5.0%
6%
4.5%
Adversrial Phstuing
39c
Standard frrsuugg BERT
Adversano Phitling NeERD
294
Standard fireturn ROTERTT
3.0%
174
2.5%
0%
fterations
(a) adversarial fine-tuning, the relative Lg dist-
(b) Trading clean accuracy off against robustness (by increasing / in Eq. ( induces increased relative L2 distance at the last epoch.
ance continuously grows as the fine-tuning proceeds.
Figure 2: Relative L2 distance between the pre-trained model and the objective model the parameter space, under different fine-tuning schemes IMDB, with small learning rate 2e-5.
in the rest of this paper, is feature extractor fine-tuned from the encoder of a pre-trained language model, e.g., Transformer [72), and q(+-)) is parameterized by using an MLP with softmaxed output. We favor classifier that is robust against ad versariaal attacks 64 i.e. maintaining high accuracy even given adversarial examples as inputs.
2.2 Adversarial Fine-Tuniie Fine-Tuning Suffers From Forgetting
Adversarial training 66
19 45
is currently the most effective defense technique
S Its training
objective can be generally formulated as:
min| E.YPP Beb(w)
max L(x, 2)]]],
where B(x)) defines the allowed perturbation space around x, and L is loss to encourage correct predictions both given vanilla samples and given perturbed examples. Such an objective function can also be applied to the fine-tuning of a pre-trained language model towards robustness, and refer to it as adversarial fine-tuning. The loss function in Eq. I can be specified following Miyato et al.
Zhang al. 79 in semi-supervised fashion as:
log (x) (KL((
where the Kullback-Leibh divergence encourages invariant predictions between and å.
Despite its effectiveness training from scratch, such an adversarial objective function may not directly fit the fine-tuning scenario. To begin with, fine-tuning itself suffers from catastrophic forgetting [46] 18 34). During fine-tuning, an objective model F.(.) tends to continuously deviate away from the traitrained one fit down-stream task [25] 81), and thus the useful information already captured before is less utilized as the tne-ttnnng proceeds. Further, adversarial fine-tuning suffers even more from the forgetting problem, the reasons of which are given what follows.
(i) Adversarial Fine- Tuning Tends to Forget: Adversarial fine-tuning targets at tackling adversarial examples, which are generally out of the manifold [38 63 of the pre-training corpora. To additionally handle them, an objective model would be fine-tuned towards a solution that is far away from the optimization starting point, i.e., the pre- pre-trained model. Figure (b) empirically shows this point: by increasing / in Eq. 2 we emphasize more on robustness instead vanilla accuracy, and consequently, at the last epoch the distance between models also increases. Besides, adversarial fine-tuning often entails more iterations to converge (several times normal fine-tuning), which further intensifies the forgetting problem, the objective model is continuously deviating away shown in Figure 2 (a).
(ii) Avversarial Fine-Tuning Needs to Memorize: Overfitting to training data dominant phe- nomenon throughout the adversarial training/fine-tung generalize better, adversarial fine-tuning should retain the generalizable features already captured
process [60] To mitigate overfitting and
by the pre-trained model [T]] 41. In addition, adversarial fine-tuning favors an objective model that

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extracts features invariant to perturbations [70] 28 for robustness. As such, all those generalizable and robust linguistic information captured during pre-training 65| are particularly beneficial and should be memorized. We empirically validates that encouraging memorization does improve both generalization and robustness in Sec. 3.5
To address forgetting, previous methods such as pre-trained weight decay 8 10 and random mixout 37 have shown their effectiveness in stabilizing fine-tuning [81]. However, they focus only on the parameter space, in which they encourage an object model to be similar to the pre-trained one. Distance in the parameter space can only approximately characterize the change in the function space, and fails to take the data distribution into consideration. A more natural way to capture the extent to which a model memorizes or forgets, should be using the mutual information between outputs of the two models, and we provide our solution as follows.
2.3 Informative Fine- Time-Tuning
We first use an information tthoooeticaee perspective look into how a pre-trained model should be leveraged throughout the whole fine-tuning process.
We define random variable T (X) as the feature of X extracted by the pre-trained language model F.)., and as the observed value of T. Similarly, we define random variable S = (X) as the feature of X extracted by our objective model (.). and the observed value of S. We formulate an overall fine-tuning objective as follows. The motivation is to train F-(-) such that S is capable of predicting Y. as well as preserving the information from T..
max I(S Y,T),
i.e. maximizing the mutual information between (i) the feature extracted by the objective model and (ii) the class label plus the feature extracted by the pre-trained model. Since the pre-trained model (-) is fixed deterministic function, the fine-tuning objective in Eq. in essense encourages F,(.) to output features that contain as much information as possible for predicting Y and T. This enables the objective model to learn from the pre- pre-trained language model via T throughout the whole fine-tuning process, and thus helps address the forgetting problem.
However, the objective defined in Eq. 3 is generally hard to optimize directly. Therefore, we decompose Eq. into two terms as follows:
I(S: Y,T) = I(S:Y) I(::T)))
(4)
where T(S; Y) measures how well the output of our objective model predict the label, and [S Y) measures when conditioned on the class label, how well the output features of the two models can predict each other. Visualization of each component can be seen in Figure for more intuitive understandings. We next introduce how each term in the right-hand side of Eq. 4 can be transformed into tractable lower bound for optimization.
(i) Maximizing I(S;Y): We treat (yyss). which is the classification layer that takes the features
extracted by the objective model input, variational distribution of p(yss), and derive variational lower bound on T(S; Y) following variational inference (33 as follows:
I(S;Y) H(Y) Enyy- log ((v|=)] (L(()))))((1s))
(5)
2 HYY) En [- [z)/)())))]
(6)
where H(Y) is a constant measuring the shannon entropy of Y, and Ex,y log {(vss)] is essentially the cross-entropy loss using {)y)s) for classification. Then, the objective of maximizing I(5; Y) can be achieved by minimizing Ex,([- log {(y[s)] instead.
(ii) Maximizing I(S;TI Y): The definition of the conditional mutual information /(S;T) Y) is as follows:
p(s, ((y
Y) Ey-po (w) [[S:T) Y = (x)) (nr [log
p(sly)p((h)) 1].
To achieve a tractable objective for maximizing Eq. 7 we employ noise contrastive estimation 20 49 and derive lower bound -Linto on the conditional mutual information. This can be summarized in the following Lemma (the proof of which can be found in Appendix A.2):

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Maximize ((S.Y)
Maximize ((S:)
Muximize I(S:T) Y)
Figure 3: Visualization of the learned geometry using 500 random samples from IMDB by t-SNE 711. and denote samples from two different classes respectively. Different colors represent different data point IDs (approximately due to limited color space). In (c) and (d), (1)x) are projected to S with radius 1, while (x) with radius 0.9. For each sub-image: (a) The geometry of (x(x) before fine-tuning, represents the data manifold. (b) Maximizing I(S;Y) Y) encourages separating the whole data manifold into two class-specific data manifolds. (c) Maximizing T(S; T) by contrastive loss in essence encourages alignment between two circles and uniformity over the whole data manifold. (d) Maximizing / ( T Y) by contrastive loss in essence encourages alignment and uniformity inside each class-specific data manifold. Best view in color with zooming in.
Lemma 1. Given { N that is sampled i.idd from po(aly), 8; Fo(xi), and 4, = Fr(x;). I( (S:T Y) is lower bounded by -Linoo E Ey--pp() B(x,,)) IN log elulssed sy croenst log N]..
and fy is a score function indexed by y-
By leveraging Lemmal I Linto can be computed using a batch of samples and then minimized for maximizing [S Y). The score function fy is defined as the inner product after non non-linear
where 9) and projections into space of hyper-sphere following 99 (a b) [a)( [a](0]al133011>> og parameterized by using MIPs.
2.4 Conditional Mutual Information Better Fits A Down-Stream Task
Above we have introduced the training objective of informative fine-tuning and decomposed it into I(S; Y) and T( Y) for optimization. However, one may wonder why not maximize I T( directly instead of [(: Y). From an information-theorrtieei perspective, we maximize I(S; YS: and TT, the intersection of the three circles in Figure [ i.e., the interaction information of them, repeatedly optimized and might induce confliction. To further elaborate this point, we look into contrastive loss and give explanation follows.
We consider contrastive learning by decomposing it into the encouragement of alignment and unifor- mity following [74). For example, maximizing ((S;T)= HS) (S is to decrease H (S)T) and increase H (S). Decreasing H (S(T)) corresponds to encouraging alignment in contrastive learn- ing, which aims to align 8; and & in the sense of inner product when they are projected into hyper-sphere. In the meanwhile, increasing (S) corresponds to uniformity in contrastive learning, which encourages 8; and s,, where i # j. to diffuse over the whole sub-space as uniformly possible. It can be seen in Figure (c) that, in the space of S alignment is achieved in that points from two different circles but with the same color are aligned, and uniformity is achieved in that all points diffuse over the whole S
However, when maximizing (S) the uniformity in the contrastive loss is encouraged over the whole data manifold. Diffusing uniformly over the whole data manifold can be against the objective of maximizing I(S: Y). which aims to separate the whole data manifold into class-specific parts as shown in Figure (b). In contrast, maximizing T(S:T) Y) only encourages uniformity inside each class-specific data manifold, which is complementary to maximizing T(S; Y), , as show in Figure 3 (d). In the meanwhile, the alignment is still enforced. More empirical support can be found in Sec.
5

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Algorithm RIFT
Input: dataset D. syper-parametent of AdamW 43 Output: the model parameters and ø
1: Initialize 0 using the pre-trained model, and initialize & and randomly.
2: repeat
3: Sample y PD (t) and then {I,, v) pp(p)y)
4: for every Zi. y in the mini-batch {Ti, v) do
5: Find é; by solving Eq.
6: end for
7: Compute the loss function defined in Eq. 11 and update 8, p, and (p by gradients.
8: until the training converges.
2.5 Robust Informative Fine-Tuning
In this section, we are going to put the objective function of informative fine-tuning into the adversarial context and introduce Robust Informative Tuning (RIFT).
To robustly train a model, the adversarial examples for training should be formulated and generated first. As our end goal is to enhance robustness in down-stream tasks, the generation process of adversarial examples should focus on preventing a model from predicting the ground truth label. However, using label for generating adversarial examples in training often induces label leaking (36), ie., the generated adversarial examples contains label information which is used as a trivial short-cut generate & in a self-supervised fashion:
by classifier. As such, follow [8 79
arg max (L((()FF(())))((()FFCC) x'eB(n)
By solving Eq. 8 f is found induce the most different prediction from that of vanilla sample in terms of KL, inside the attack space B(x).
Next, we introduce how to robustly optimize each objective in the informative fine-tuning by using a.
(i) Robustly Maximizing I(S;Y): As shown in Sec. 2.3 maximizing I I(S; Y) can be achieved by minimizing cross- cross-entropy loss instead. To encourage adversarial robustness, this cross-entropy loss can be upgraded encourage both correct predictions on and invariant predictions between and We formulate such an objective function follows:
48 79
min Ly-task:
Cyessk
E
[- log q(vl (x))) + (BL(((F((()))(<(155)))))
(9)
2,yPPD
where denotes the parameters F.(-) and @ denotes the parameters of g(++). Minimizing Cr-msk in Eq. 1 corresponds to adversarilly maximizing I(SYY) for robust performance in down-stream task. By doing so, the adversarial example generated by Eq, S first and then both and are used to optimize the model parameters 0 and Φ.
(ii) Robustly Maximizing I(SITI Y): We aim to maximize the conditional mutual information I[ Y), but under an adversarial distribution of input data. We formulate such a term as I(Š:T|Y), Y), where random variable $ Fx(X), X Paáv (ilu, 0, di BB, and sampling from Padva the adversarial distribution of input data, is to generate adversarial example & by Eq. S
To optimize 1($;T) Y), we propose -Crintto as lower bound it, and formulate the objective to minimize Lriinto as follows (similar to - -Lnnto by using Lemma
N
log (F(.t))))) Σ efo(3.)))) log NJ].
min Crinfo 0,5
Criino
E p-pD(y)
(10)
(a.
where 3 F.(2:), 44 F((x,, and y denotes the parameters of all the score functions fy- By Eq. 10 are able to encourage F-(-) to retain information from F:(-) in robust fasion.
Noted that, Lrinfo we do not use á; to extract features from the pre-trained model. This follows the spirit of Knowledge Distillation (2) (though the setting is different in that student is expected to perform identically to teacher in knowledge distillation, while in fine-tuning does not): the data used to extract features of teacher should be inside the domain for which the teacher is trained. A

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Table Accuracy(%) of different fine-tuning methods under attacks on IMDB.
Method
Model
Genetic
PWWS
Method
Model
Genetic
PWWS
Standard
BERT
12.5
40.7=1.1
Standard
RoBERTa
42.1
45.6a3.1
Adv-Base
BERT
74.8
68.3
Adv-Base
RoBERTa
70.3 63.3
Adv-PTWD
BERT
73.9.0.4
29.1.a.7
Adv-PTWD
RoBERTa
69.3.1.4
64.4.0.3
Adv-Mixout
BERT
75.4
68, 68.8.0.6
Adv-Mixout
RoBERTa
70.6.1.
63.9
RIFT
BERT
77.2
=0.
RIFT
RoBERTa
73.5
66.3
(a) Accuracy (%) based on BERT-base-uneased.
Accuracy (%) based RoBERTa-base.
Table 2: Accuracy($b) different fine-tuning methods under attacks on SNLL
Method
Model
Genetic
PWWS
Method
Model
Genetic
PWWS
Standard
BERT
40.7
19.420.4
Standard
RoBERTa
43.4+1.2
20.4
Adv-Base
BERT
75.720.5
72.920.2
Adv-Base
RoBERTa
82.6.0.6
79.920.7
Adv-PTWD
BERT
75.2+1.0
72.620.5
Adv-PTWD
RoBERTa
81.2
78.9
Ady-Mixout
BERT
76.320.8
73.221.0
Adv-Mixout
RoBERTa
82.620.9
80.620.3
RIFT
BERT
77.5.0.9
74.3a1.1
RIFT
RoBERTa
83.5
81.1
(a) Accuracy (%) based on BFRRT-base-uncased.
(h) Accuracy (%) based RoBERTa-hase.
down-stream NLP task, e.g. sentiment analysis, has data domain that is generally sub-domain of the pre-training corpora, while à can be significantly off the pre-training data manifold.
Objective Function of Robust Informative Fine- Tuning (RIFT): The objective function of RIFT a combination of Cystsk and Leinno. defined as follows:
min tr + OtCrrinfo 0.0.0
(11)
where the hyper-parameter a controls what extent we encourage an objective model absorb information from the pre-trained one (ablation on a can be seen in Sec. 3.5). We summarize the whole training process Algorithm and include more implementation details in Appendix A.1.
Experiments
3.1 Experimental Setting
Tasks and Datasets: We evaluate the robust accuracy and compare our method with the state-of-the-
arts on: (i) Sentiment analysis using the IMDB dataset [44) (ii) Natural language inference using the SNLI dataset [6]. We mainly focus on robustness against adversarial word substitutions 58). as such attacks preserve the syntactic and semantics very well [3] 78 [2]. and are very hard to detect even by humans. Under this attack setting, any word in the input sequence can be substituted by semantically similar word of it (often its synonym). We evaluate robustness on 1000 random examples from the testset of IMDB and SNLI respectively following 12).
Model Architectures: We examine methods and compare with state-of thtt-oofthhearsss the following two prevailing pre-trained language models: (i) BERT-baas-uncased [1]. (ii) RoBERTa-base [41].
Attack Algorithms: Two powerful attacks are employed: (i) Genetic 2 based population
algorithm. Aligned with [2]. the population size and iterations are set as 60 and 40 respectively. (ii) PWWS [58) based word saliency. We only attack hypothesis SNLI aligned with [31 [2].
Substitution Set: We follow [3 to use the substitution set from
2 and the same language
model constraint is applied to Genetic attacks and not to PWWS attacks.
3.2 Comparative Methods
(i) Standard Fine-Ture Tine-Tuning: The standard fine-tuning process first initializes the objective model by the pre-trained weight, and then cross-entropy loss to fine-tune the whole model.
7

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72
Accurecy under Genetic (16)
RFF
RFT Adv-Misou
Adv-Miouu Advotians
68
66
Accuracy
84
db
Varilla accuracy
Varilla accuracy 196)
(a) Accuracy (%) under Genetic attacks.
(b) Accuracy (%) under PWWS attacks.
Figure 4: Tradeoff curve between robustness and vanilla accuracy of BERT-based model on IMDB.
Table 3: Accuracy(%) of RIFT with maximizing Y(S;TT)) and 1(5 respectively.
Maximizing
Model
Genetic
PWWS
Maximizing
Model
Genetic
PWWS
I(S:T) Y)
BERT
77.2
70.1
I(S: Y)
BERT
77.5
74.3
I(S:T)
BERT
76.1
69.4
I(SIT)
BERT
76.6
72.1
I(S:T))
RoBERTa
73.5
66.3
I(S;T) Y)
RoBERTa
83.5
81.1
I(SIT)
RoBERTa
72.0
65.3
I(SIT)
RoBERTa
82.5
79.4
Accuracy (%) under attacks IMDB.
(b) Accuracy (%) under attacks SNLI
(ii) Adversarial Fine-Tuning Baseline (Adv-Base): We employ the sate-offtheeart defense against word substitutions, ASCC-Defense [2], as the adversarial fine-tuning baseline. This method is not initially proposed for pre-trained language models but can readily extend to perform adversarial fine-tuning. During fine-tuning, adversarial example 2, which is sequence of convex combinations, generated first and then both and & used for optimization using objective defined in Eq. 9
(iii) Ady Pre-Trained Weight Decay (Ad=-PTW)): fre-trained weight decay 8 10 penalizes X| aaj Wee 22 and mitigates catastrophic forgetting [75 37]. We combine it with the adversarial baseline for comparisons and À chosen as 0.01 on IMDB and 0.005 on SNLI for best robustness.
(iv) Ady + Mixout (Adv-Mixout): Motivated by Dropout 62 and DropConnect 73 Mixout [37
proposed to addresses catastrophic forgetting in fine-tuning. each iteration each parameter replaced by its pre-trained counter-part with probability fine-tuning baseline for comparisons with our method and m chosen as 0.6 for best robustness.
We combine it with the adversarial
The proposed adversarial fine-tuning method. It can be deemed the adversarial fine-tuning baseline plus the Lrinfo term. We as 0.2 for all score functions fy. For best robust accuracy, is chosen as 0.1 and 0.7 on IMDB and SNLI respectively. Ablation study on a can be seen in Sec. 3.5
(v) Robust Informative Vine-Tuning (RIFT):
For fair comparisons, all compared adversarial fine-tuning methods use the same f on a same dataset, i.e., 3 10 IMDB and /3 5 SNLI, both of which are chosen for the best robust accuracy. Early stopping [60] is used for all compared methods according to best robust accuracy More implementation details and runtime analysis can be found in Appendix A. and A.3.
3.3
Main Result
In this section we compare our method with state-of-the-arts by robustness under attacks. As shown in Tables T and 2 RIFT consistently achieves the best robust performance among state-of-the-ards in all datasets across different pre- -reitra language models under all attacks. For instance, on IMDB our method outperforms the RoBERTa-baen based runner-up method by 2.9% under Genetic attacks and 1.9% under PWWS attacks. On SNLI based on BERT, we surpass the runner-up method by 1.2% under Genetic attacks and 1.1% under PWWS attacks. addition, RIFT consistently improves robustness upon the adversarial fine-tuning baseline, while the improvements by other methods are not stable. We observe that on SNLI, we surpass the runner-up by relatively small margin compared to IMDB. This may relate to the dataset property: input from SNLI has smaller attack space (on average 6.5'

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Table 4: Ablation study on the hyper-parameter a.
Parameter a
Vanilla
Genetic
PWWS
Parameter a
Vanilla
Genetic
PWWS
0.00
78.1
74.8
68.3
0.00
79.4
75.7
72.9
0.05
78.4
76.5
69.2
0.50
80.0
76.9
73.8
0.10
78.3
77.2
70.1
0.70
80.4
77.5
74.3
0.30
78.3
76.2
69.5
1.00
80.6
77.3
73.2
(a) Acc (%) of RIFT based on BERT on IMDB.
(b) Ace (%) of RIFT based on BERT on SNLI.
combinations of substitutions on SNLI compared to 620H on IMDB), and thus smaller absolute space left for improvement. For results of vanilla accuracy please refer Appendix A.4.
3.4 Conditional Mutual Information Does Fit a Down-Stream Task Better
In this section empirically validate that maximizing [(S; ()) cooperates with a down-stream task better than maximizing I(S does. We plot two sets of results of our method with max- imizing [(S;T] Y) and T() respectively (using similar noise contrastive loss with all other hyper-pprameters the same). As shown in Table RIFT with maximizing [(S; T] Y) consistently outperforms that with maximizing I(S;T) in terms of robustness under all attacks both IMDB and SNLI, which serves as an empirical validation Sec. 2.4
3.5 Ablation Study and Tradeoff Curve Between Robustness and Vanilla Accuracy
In this section conduct ablation study on a, which is the weight of Cr-ino in Eq. It aims to control to what extent the objective model absorbs information from the pre-trained one. As shown in Table 4 good value of improves both vanilla accuracy and robust accuracy; e-g., IMDB increasing from 0 to 0.1 results in increased vanilla accuracy by 0.2% and increased robust accuracy under Genetic attacks by 2.4%. This demonstrates that RIFT does motivate the objective model to retain robust and generalizable features that are beneficial to both robustness and vanilla accuracy. When goes too large, the objective of fine-tuning would focus too much on preserving as much information from the pre-trained model as possible, and thus ignores the down-stream task.
One may wonder whether informative fine-tumm tie-ttnnngg itself can improve the clean accuracy upon normal fine-tuning. The answer is yes; e.g., on IMDB using RoBERTa, informative fine-tumin tine-tuning improves about 0.3% clean accuracy upon normal fine-tum fine-tuning baseline. The improvement is not very significant as normal fine-tuning targets at vanilla input only and thus suffers less from the forgetting problem. One common problem in contrastive loss is that the complexity grows quadratically with N, but larger N contributes to tighter bound on the mutual information
One potential improvement maintain dictionary updated on-the-fly like (21) and we will leave for future exploration.
(4 53
We finally show the trade-off curve between robustness and vanilla accuracy Figure As shown, RIFT outperforms the state-of- tiee--ffthhhrrttt the-arts in terms of both robust accuracy and vanilla accuracy. It again validates that RIFT indeed helps the objective model capture robust and generalizable information to improve both robustness and vanilla accuracy, rather than trivially trading off one against another.
4 Related Work
Pre-Trained Language Models:
Language modeling aims at estimating the probabilistic density of the textual data distribution. Classicial language modeling methods vary from CBOW, 3kip-Gram
[7]. to GloVe (5l) and ELMo [52]. l. Recently, Transformer [72] has demonstrated its power in
language modeling, e.g., GPT [55] and BERT [IT]. The representations learned by deep pre-trained
language models are shown to be universal and highly generalizable [77] 41, and fine-tuning
pre-trained language models becomes a paradigm in many NLP tasks 255
Adversarial Robustness: DNNs have achieved success in many fields, but they are susceptible to adversarial examples (44. In NLP, attacks algorithms include char-level modifications 24 13
15 54 sequence-level manipulations (29
59
30
82 and adversarial word substitutions
78 For defenses, adversarial training 64 19 generates adversarial examples
on- nn-thh--ly fly and is currently the most effective. Miyato et al. 48 first introduce adversarial training to

---

of Education (MOE) Academic Research Fund NLP using L2-ball to model perturbations. and later more scometry-alignee methods like first-order approximation 13,, axis-aligned bound [3T 27]. and convex hull [1]] become favorable.
The fine-tuning of pre-trained language models 25 can be very unstable [T], as during fine-tuning the objective model can deviate much from the
Fine-Tunin Tine-Tuning and Catastrophic Forgetting:
pre-trained one, and easily over-fits to small fine-tuning sets [25]. Such phenomenon is referred as
catastrophic forgetting [46. [8] in fine-tuning (25 37 81). Methods to address catastrophic forgetting
include pre-trained weight decay
g
10 81. learning rate decreasing [25]] and Mixout regularization
33.. These methods focus on parameter space to constrain the distance between two models while our method addresses forgetting following an
perspective. In continuous [42 [6 39 [ and regularization- based [34] [4] [26] also aims
learning, method like rehearsal-
at the forgetting problem, but under a different setting: they focus on balanced performance on tasks with previous data, while in our setting the pre-training corpora are not available and language modeling is not our concern.
has demon- strated its power in learning self-supervised representations ( (21]. The contrastive loss such as InfoNCE[4]], InfoMax [23, is proposed as maximizing the mutual information of features in dif- 69 66 [74]. This paper shares the same perspective
Contrastive Learning and Mutual Information:
Contrastive learning
49
23 61
ferent views, and further discussed in [53 67
of information theory, but looks into a different problem, addressing forgetting for fine-tuning. Addressing forgetting motivates our objective of maximizing the mutual information between the outputs of two models, which is different from conventional contrastive learning, and cooperation with fine-tuning motivates the use of conditional mutual information, which is quite different also. Fischer and Alemi [16] propose to restrict information for robustness in vision domain. Restricting information to some extent helps model ignore spurious features, but may be at the cost of ignoring robust features as well. contrast, the proposed method aims to retain the generic and robust features that are already captured by pre-trained language model, and thus better fits a fine-tuning scenario.
5 Discussion and Conclusion
In this paper, we proposed RIFT to fine-tune a pre- trained language model towards robust down-stream performance. RIFT addresses the forgetting problem from an information thhoorettceee perspective and consistently outperforms state-of-the-arts in experiments. We hope that this work contribute to the NL.P robustness in general and thus to more reliable NI.P systems in real-life applications.
Acknowledgements
This work is supported by the Singapore Ministry (AcRF) Tier 1 (S21/20) and Tier 2.
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14


Reviews:
[{"review_id": "W9Br0Ese4Ni", "reviewer": "Reviewer_aWT9", "summary": "This paper first demonstrated that adversarial training does not directly fit a conventional fine-tuning scenario proposed, and then proposed an adversarial fine-tuning method (Robust Informative Fine-Tuning RIFT) from an information-theoretical to fine-tune a pre-trained language model towards robust down-stream performance.\n\nThe experimental results on two prevailing NLP tasks (i.e., sentiment analysis and natural language inference) show that RIFT outperforms some baseline fine-tuning methods in terms of robustness.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Clarity: \nThis paper is clearly written and well organized.\n\nOriginality: \nThis work shows an interesting analysis, that is, the adversarial training fails to retain the generic and robust linguistic features that have already been captured by the pre-trained model. They also proposed an informative adversarial fine-tuning method that maximizes the mutual information between the output of an objective model and that of the pre-trained model conditioned\n on the class label.  The work is inspiring in the robustness of  NLP and easy to follow.\n\nSignificance:\n This work is technically sound, and all claims are well supported by both theoretical analysis and experimental results. \n\nConcerns:\n- What is the connection between the relative L2 distance (between the pre-trained model and the objective model in the parameter space) and catastrophic forgetting? Does greater L2 distance necessarily mean more forgetting?\n- How does RIFT perform on the General Language Understanding Evaluation (GLUE) benchmark?\n\n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes, the authors adequately addressed the limitations and potential negative social impact of their work.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": ["Privacy and Security (e.g., consent)"], "time_spent_reviewing": "About 2 hour. "}, "extra_text": {}}, {"review_id": "MblRO8TQSTQ", "reviewer": "Reviewer_gPjn", "summary": "This paper proposes a new method for finetuning pretrained NLP models to achieve both high standard and high robust accuracy. They argue that finetuning on the more difficult and plentiful adversarial examples can lead to catastrophic forgetting, more so than standard finetuning of models. They propose an additional loss wherein the model is encouraged to get high accuracy while also retaining features from the pretrained weights. This improves the accuracy over standard finetuning and various other ways of regularizing adversarial finetuning.", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n* I like the motivation and early sections showing that adversarial finetuning shifts the model from its original weights by a much larger amount that standard finetuning.\n* There are clear gains across multiple tasks for their method.\n\nWeaknesses:\n* My main concern is that the baselines are weak. In particular, the idea of overfitting in adversarial training is well-established  and past work has shown that weight decay, dropout, and these forms of standard regularization are insufficient (https://arxiv.org/abs/2002.11569). Instead of these, early stopping for robust accuracy is the best strategy for improving results. What I would like to see is an early stopping baseline, and what would be even better would be learning curves that show the standard and robust accuracy over time for different finetuning methods. Ideally, the paper's proposed method, if stopped at the proper time, is superior to early stopping using standard finetuning or other regularization strategies.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2"}, "extra_text": {}}, {"review_id": "Gg7W2ZJV7es", "reviewer": "Reviewer_CwDX", "summary": "This paper studies the catastrophic forgetting problem when fine-tuning pre-trained language models towards adversarial robustness. They argue that existing robust training methods, such as adversarial training, make the objective model deviated too much from the pre-trained model, resulting in bad generalization. Starting from an information-theoretical perspective, the authors propose RIFT, a fine-tuning method that maximizes the mutual information between the outputs of the objective model and the outputs of the pre-trained model when conditioned on the labels. The experimental results show that RIFT has a better performance than other baselines, which supports the better generalization as the authors claim. In addition, they give explanations why considering I(S;T|Y) in the objective is better than considering I(S;T).\n\n\n==== After Rebuttal ====\n\nThanks for the response. All my questions are properly resolved.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Pros:\n- The motivation is clear and convincing.\n- The proposed method has a theoretical support.\n\nCons:\n- The improvement of SNLI seems to be small. Providing the significance test may help.\n\n\nSome questions I have:\n- What norm-ball B(x) you consider in the experiments? What's the size of B(x)\n- Are the adversarial examples you generate on the embedding-level or token-level? My understanding is that you only find an adversarial perturbation on the embeddings without generating the actual adversarial text.\n- Can the proposed method be extended to token-level NLP tasks, such as sequential tagging?\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5"}, "extra_text": {}}, {"review_id": "8D9JBiK8nMf", "reviewer": "Reviewer_8Qx5", "summary": "The paper studies the problem of adversarial fine-tuning of pretrained NLP models, that is fine-tuning them in a way that makes them robust to adversarial attacks. It proposes a new method called RIFT. RIFT alters the usual adversarial training by adding a regularization term that maximizes the conditional mutual information between feature vectors produced by the original pretrained model and the currently fine-tuned one. The motivation is to alleviate catastrophic forgetting. The experiments show RIFT getting a slightly higher adversarial robustness than the baselines methods, such as e.g. using the L2 penalty instead of the proposed regularizer. \n", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Originality: the method draws inspiration from the recent unsupervised representation learning with contrastive losses. The applications of these methods to regularizing adversarial training appears to be novel. \n\nQuality: the experimental part of the paper appears to be technically sound. There are ablation experiments that confirm the significance of the key aspects of the method. Same probably holds for proofs, but some steps were skipped, see my comments on clarity.\n\nClarity: the paper is mostly clearly written. Here are some questions/concerns:\n- The score function $f_y$ and the MLPs $g^1_y(a)$ and $g^2_y(a)$ are not explained very clearly. How are they trained? What is their impact on the correctness of Lemma 1? The proof in Appendix 1 just skips this part. \n- Some choices of terms are somewhat confusing. I’d recommend “downstream model” instead of “objective model”, “the feature vector” instead of “the feature”, “state-of-the-art methods” instead of “state-of-the-arts”\n- The discussion in Section 2.4 should be compressed and be more to the point. Phrases like “Such a view provides some intuitions, but it is still vague in the sense of what is actually going on.” are not helpful in scientific writing.\n- To put the reported results in context, it would be helpful to show the “vanilla accuracy” without adversarial training for all the employed dataset. For example, for SNLI I found a BERT baseline of 90%, where Table 6a in Section A.4 reports ~80%. \n\nSignificance: I find the results to have a non-negligible but limited significance. The adversarial robustness improves 2-3% from around 70-80%. That appears to still come at the cost of a significant decrease of vanilla accuracy (unclear how big though - see my clarity comments). But the baseline is even worse. \n\nOther comments:\nThe RIFT regularizer is proposed with continual learning motivations in mind. It would be very interesting how a regularizer like that performs on continual learning benchmarks. \nIt would be highly interesting to see the impact of the batch size on the performance. The batch sizes that the paper uses are surprisingly small, especially given that the contrastive loss of RIFT appears to need a large batch size. \n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Within the scope of improving adversarial robustness of NLP models, the only limitation of the paper is that the improvements, while being significant, are not very big. The scope of the evaluation (focusing on NLP only) can be considered another limitation. \n\nThe societal impact of having more robust models should be rather positive.\n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3.5"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This work provides a well-motivated approach to fine-tune a model for robustness while retaining features from pretrained weights. Adversarial robustness in language is an important problem since in general it's trivial for an attacker to slightly change the input. The paper is well-written and the analysis is interesting. The method is well supported theoretically and there is a good set of experiments, however, the improvements in robustness have limited significance while there is still a drop in accuracy on the vanilla evaluation. The rebuttal was well-written and answered the reviewers' questions.
Decision: Accept (Poster)