Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Authors: Haoqi Zheng
Qihuang Zhong
Liang Ding
Zhiliang Tian
Xin Niu
Changjian Wang
Dongsheng Li
Dacheng Tao
Venue: EMNLP 2023 Main (2023)
Keywords: self-evolution learning
mixup
pretrained language model
few-shot text classification

Abstract: Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup merges sample pairs to generate new pseudos, which can relieve the data deficiency issue in text classification. However, the quality of pseudo-samples generated by mixup exhibits significant variations. Most of the mixup methods fail to consider the varying degree of learning difficulty in different stages of training. And mixup generates new samples with one-hot labels, which encourages the model to produce a high prediction score for the correct class that is much larger than other classes, resulting in the model's over-confidence. In this paper, we propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification, which can generate more adaptive and model-friendly pseudo samples for the model training. SE caters to the growth of the model learning ability and adapts to the ability when generating training samples. To alleviate the model over-confidence, we introduce an instance-specific label smoothing regularization approach, which linearly interpolates the model’s output and one-hot labels of the original samples to generate new soft labels for label mixing up. Through experimental analysis, experiments show that our SE brings consistent and significant improvements upon different mixup methods. In-depth analyses demonstrate that SE enhances the model's generalization ability.

Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Haoqi Zheng", Qihuang Zhong Liang Ding', Zhiliang Tian
Xin Niu Changjian Wang Dongsheng Li', Dacheng Tao
'College of Computer, National University of Defense Technology
School of Computer Science, Wuhan University 'JD Explore Academy *University of Sydney
Abstract
et al., 2023; Peng et al., 2023; Lu et al., 2023c). However, empirical studies (Zhong et al., 2023) suggest that LLMs do not always outperform BERT in some language understanding tasks. Hence, employing BERT is still a viable option in some applications. Text classification tasks often en- counter few shot scenarios (e.g. NLI and Para- phrase tasks), where there are limited suitable la- beled data available for training. Data augmen- tation (DA) generates new data by changing the original data through various methods, which en- larges the training dataset to alleviate the issue of data scarcity.
Text classification tasks often encounter few- shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data aug- mentation with mixup merges sample pairs generate new pseudos, which can relieve the data deficiency issue in text classifica- tion. However, the quality of pseudo-samples generated by mixup exhibits significant vari- ations. Most of the mixup methods fail to consider the varying degree of learning dif- ficulty in different stages of training. And mixup generates new samples with one-hot la- bels, which encourages the model to produce high prediction score for the correct class that is much larger than other classes, resulting in the model's over-confidence. In this paper, propose a self-evolution learning (SF) based mixup approach for data augmentation in text classification, which can generate more adap- tive and model-friendly pseudo samples for the model training. SE caters the growth of the model learning ability and adapts to the ability when generating training samples. ate the model over-confidence, we introduce an instance-specific label smoothing regular- ization approach, which linearly interpolates the model's output and one-hot labels of the original samples to generate new soft labels for label mixing up. Through experimental analysis, experiments show that our SE. brings consistent and significant improvements upon different mixup methods. In-depth analyses demonstrate that SE enhances the model's gen- cralization ability.
In text classification tasks, DA methods can be divided into two categories: DA meth- ods like EDA (Wei and Zou, 2019). Back- Translation (Kobayashi. 2018), and others based synthesis such mixup. The first category con- ducts DA by altering only the inputs. These meth- ods only alter the inputs to generate new data while maintaining the original labels. These methods are easy to implement, but the input only changes a little thus leading to augmented inputs with limited diversity, which may reduce model generalization. The second category of DA methods modify both inputs and labels, which changes the input sam- ples in a certain way and simultaneously changes the corresponding labels compose new sam- ple. These methods tend to generate samples more distinct from the original samples.
allevi-
Mixup is a DA method that modifies both in- puts and labels. It mixes up inputs of samples and their labels, where labels are commonly repre- sented with one-hot encoding. Most of these meth- ods mix up inputs of two samples on their input text (Yun et al., 2019) hidden-level representa- tions (Verma al., 2019). However, the pseudo sample, simply combined with two samples, may not be adaptive to the model's learning ability and friendly to the model training. Recently, some work (Sawhney et al., 2022; Park and Caragea, 2022)
1 Introduction
Recently, generative large language models (LLMs) have won great popularity in natural lan- guage processing (NLP), and have achieved im- pressive performance on various NLP tasks (Kocoñ
'Haoqi Zheng and Qihuang Zhong contribute equally to this work.
Conresponding Authors

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have focused on selecting similar sample pairs for the mixup. Sawhney al. (2022) select samples according to the embedding similarity. Park and Caragea (2022) merge one sample considering the confidence of the model's predictions. Moreover, in few-shot scenarios, using hard labels (one-hot labels) can lead to issues, where the one-hot labels fail to provide uncertainty of inputs since all the probability mass is given to one class. This results in over-confident models since the largest logit be- comes larger than the others which removes the uncertainty of label space (Szegedy et al., 2016). The current label smoothing techniques generate soft labels that cannot dynamically adapt to the model's 's increasing ability as the training goes on, so they also cannot adjust according to the model's performance at the current stage.
on few-shot text classification tasks.
2 Related Work
2.1 Few-shot Text Classification
Driven by the observation that humans can rapidly adapt existing knowledge to new concepts with limited examples, few-shot learning (Fei-Fei al., 2006) has recently drawn lot of attention. Few- shot text classification entails performing classifica- tion after training or tuning a model on only a few examples. Several studics (Yu et al., 2018; Bailey and Chopra. 2018; Geng et al., 2020) have explored various approaches for few-shot text classification, which mainly involve the traditional machine learn- ing techniques for selecting the optimal category sub-samples.
More recently, ever since Devlin et al. (2019); Brown et al. (2020) show the impressive perfor- mances of pre- troitraindd language models (PLMs) on variety of NLP tasks, a great deal of works (Wu et al., 2019; 9; Bansal et al., 2020) tend to employ the PLMs to tackle the few-shot text classification problem. One line of work aims fine-tune the PLMs (mainly for discriminative PLMs, such BERT (Devlin et al., 2019)) with the few-shot train- ing data. Correspondingly, how to design data aug- mentation methods to better enrich training data has become the focus of these works. Rather than fine- tuning the PLMs, separate line of research aims to take full advantage of the emergent few-shot learning ability of larger PLMs, i.e., GPT-3 (Brown et al., 2020) and InstructGPT (Ouyang al., 2022), and use the few-shot training data the demon- strations for performing in in-context learning process (Lu et al., 2023b,a). Considering the BERT-based PLMs are more suitable for text classification tasks, we follow the former research line and focus on exploring the ability of BERT-basce PLMs in the few-shot text classification.
In this paper, we propose self-evolution learning for mixup to achieve data augmentation in text clas- sification tasks. To cater to the model's learning ability, we first divide the training data into easy- to-learn and hard-to-learn subsets. We gradually start from the mixup of easy-to-learn samples and then gradually transition to the mixup operation of hard-to-learn samples. To avoid the model's over- confidence, we introduce an instance-specific label smoothing method, where we linearly interpolate the predicted probability distribution of the orig- inal sample and its one-hot label to obtain soft label. Using this soft label reduces the difference between the model's prediction probability for dif- ferent classes, which can alleviate the model's 's over- confidence. Additionally, this instance-specific la- bel can dynamically adapt to the growth of the model's 's increasing ability and can be customized to the model's current performance. Our method has empirically proven that mixing up in the order of increased difficulty can make the generated sam- ples more adaptive for model training compared to randomly selected samples.
Our contributions are as follows:
2.2 Data Augmentation in NLP
We propose self-evolution learning (SE) for mixup consider the learning difficulty of samples for data augmentations on text classification tasks.
Since the bottleneck in few-shot learning is the lack of data, the performance can be easily improved if we can generate more labeled data. Hence, various NLP data augmentation techniques have been pro- posed, such as EDA (Wei and Zou, 2019), Back- Translation (Kobayashi, 2018) and CBERT (Wu et al., 2019). These methods show remarkable per- formance in some specific scenarios, however, they mainly focus on altering the original input, result- ing in a lack of diversity in the generated samples.
We propose an instance-specific label smooth- ing approach for regularization which can obtain dynamic and adaptive soft labels to alleviate the model's ' over-confidence and enhance the model's generalization ability.
Extensive experiments show that our model sig- nificantly and robustly improves the mixup method

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Stage11 Divide the Data
easy
Inbec
BERT
Difficulty
Calculate
hard
pass
forward
casy
gradient
hard
propegation
Stage2: Mixup the Samples
mixup the casy samples
A.J
Loss
+(1-2)
BERT
mixup the hard samples
+(1-1)
Logg
L
+
Figure 1:
Overview of mechanism, which contains stages: using existing BERT divide the data according to difficulty and mixing the samples to model's 's training from easy to hard. Best viewed in color.
In response this problem, Szegedy al. (2016) first propose domain-independent data augmenta- tion technique (i.e,, mixup) in the computer vision domain, that linearly interpolates image inputs on the pixel-based feature space. Guo et al. (2019) then integrate the mixup with CNN and LSTM for text applications. Furthermore, to achieve better performance, various works (Sun et al., 2020; Cao et al., 2021; Chen al., 2020; Yoon et al., 2021; Zhang et al., 2022) attempt to improve the mixup technique from two perspectives: 1) how bet- ter merge the two hidden representations, and 2) how to directly perform the mixup on the input sentences.
paper, improve the mixup with simple- yet-effective self-evolution learning mechanism.
Method
3.1 Overview
For the text classification task in the few-shot sce- nario, we propose data augmentation method via mixup, where the training follows easy-to-hard schedule over the augmented data. First, struct text classification model based on the BERT and then employ a mixup method for data augmen- tation expand the amount of data (Sec3.2). To make the mixup adaptive for model learning abil- ity, we propose self-evolution learning for mixup (Sec3.3). To alleviate the over-confidence prob- lem of the model, we propose instance-specifie label smoothing regularization method, which linearly interpolates the model's outputs and hot labels of the original samples to generate new soft labels as the label for mixing up (Sec3.4).
Although achieving remarkable performance, these previous mixup strategies still have some lim- itations. Specifically, they (usually) randomly se- lect samples to mix and do not consider the model's learning ability. Some works (Sawhney et al., 2022; Park and Caragea, 2022) have also focused on ad- dressing this issuc and proposed various methods for effectively choosing samples. Sawhney al. (2022) select samples according the embedding similarity. Park and Caragea (2022) merge one sample considering the confidence of the model's predictions. Along the same research line, in this
3.2 Text Classification Model and Mixup
We utilize the BERT (Devlin al., 2018) for text classification tasks, where the BERT model adopts a multi-layer bidirectional Transformer encoder

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architecture and is pre-trained on plain text for masked language modeling.
Algorithm 1 Mixup with Self-evolution learning
and instance-specifie label smoothing
BERT takes a sequence of words as the input and outputs the representation of the sequence. For text classification tasks, BERT takes the final hidden state h of the first token [CLS] as the sentence rep- resentation. Then, we append a softmax function with linear transformation to generate probabil- ity distribution and the predicted label.
Input: Labeled set D; Mixup function <());
Instance-specinn label smoothing function x(-) // Stagel: Divide the Data // Calculate Difficulty
for (2t,, 36) in D do
d(r:) = (p(2))2))) - maxycCa/u max end median(d(x:)) for (2i 36) in D do Deasy - Deasy U (k), Dhard + Dhard U (Zi, 91), end // Stage2: Mixup the Samples for (2 3/) in D Deasy do
To relieve the data deficieney in few-shot sce- narios, we propose a data augmentation method to generate pscudo samples for training the BERT model. The core idea of mixup is to select two labeled data points (04, 3/) and (Iy, Mj), where x is the input and is the label. The algorithm then produces new sample (ē, 3) through linear inter- polation:
if d(xs) <
if d(z;) > E
1j Zarg max cos (12224))
y. ((11)
v o(15)
ã = Ao; + (1 A)az
(1)
F. ((I),
ÿ = Aya + (1 A)ys
(2)
Di <(1/: 8//)
where À [0,1] denotes the mixing ratio of two samples.
end
for
(I y/ in Dhard do
Ij = Farg max cos (z2aa))
y (bv))
3.3 Self-Evolution Learning for Mixup
To make the mixed samples more adaptive and friendly to the model training, we propose a novel mixup training strategy: progressive mixup training from easy to hard. This idea is inspired by human learning behavior: human's learning schedule usually starts from easier tasks and gradually pro- gresses more challenging tasks. We first propose the degree of difficulty to measure the difficulty of the model in learning samples and then conduct mixup in two stages: (1) dividing the dataset based on the degree of difficulty, and (2) mixup two sam- plcs according to the order of difficulty from casy to hard.
5. ((1);
Ve <(1l: 1/)))
end Output: {(Tg, 1:)
sample as mentioned in Eq.3. Then, we use the median of the degree of difficulty to partition the dataset: we assign samples with a degree of dif- ficulty less than the median to the easy-to-learn dataset Deasy. and samples with the degree of diffi- culty greater than the median to the hard-to-learn dataset Dhard-
To obtain the degree of difficulty d(x;;) for sam- ple Ki, we calculate the difference between the model predicted probability on the correct label p(ys (x)) and the maximum predicted probability among the wrong labels as Eq.3:
In the second stage of self-cvolution learning. we conduct mixup from Denvy to Dhard- For easy- to-learn data, we perform mixup operations on the Deasy- Given a sample x; from Deasy, we search for the most similar sample 2; in Deasy. where the similarity is measured by cosine similarity. Then, we mix the two samples up by interpolating the inputs (z, and Izy) and labels (y, and yj) Eq.1 and Eq.2. The data selected according the above process then used for training, and the resulting generated data is added to the model training. In the hard-to-learn dataset, we follow the same way that selects two most similar samples and mixup to
d(x:) = (p(y6|=;)- - max p(ylz;)), (3) vEC.
where yi denotes the ground-truth label, and C denotes the set of all candidate labels.
In the first stage of self-evolution learning (SE), we divide the training data into two datasets ac- cording to the degree of difficulty. Given a training set D, we calculate the degree of difficulty of each

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compose a pseudo sample. The sample serves as new sample augment the training data, Algo- rithm I summarizes the above procedure.
Finally, in the SE training stage, we employ the cross-entropy loss as follows:
1 m
LLS
log
(6)
3.4 Instance-Specific Label Smoothing for Regularization
To avoid over-confidence caused by hard labels in few-shot scenarios, we propose a novel instance- specific label smoothing (ILS) approach to adap- tively regularize the training and improve the gen- eralization ability of the classification model.
4 Experiments
4.1
Datasets
To investigate the effectiveness of our method, we conduct extensive experiments on various lan- guage understanding tasks, including diversity of tasks from GLUE (Wang et al., 2018), Super- GLUE (Wang et al., 2019) and other benchmarks, iee, sentiment analysis (SST-2, Rotten tomato), natural language inference (RTE, CB), paraphrase (MRPC), and text classification (SUBJ, Amazon counterfactual), To simulate the few-shot scenarios, we randomly select 10 samples per class from the training set for each task, and use them for training the models. For evaluation, we use the Accuracy as the metric and report the averaged results over random seeds to avoid stochasticity. Due the space limitation, we show the details all tasks and datasets in Appendix A.1 (Table 5).
The traditional label smoothing (LS) approach replaces the hard label distribution yi with va as Eq. 4, where v/ is a mixture of the original label distribution y/ and a distribution uz- The Uj usu- ally a uniform distribution.
1/4 = (1 - a) y + ou,
(4)
Traditional LS lowers the value of the correct label and increases all others, which successfully prevents the largest predicted score much larger than all others (Szegedy et al., 2016). However, in the traditional LS, the distribution of is fixed and cannot dynamically generate labels to adapt to the model learning.
4.2 Implementation Details
Motivated by this observation, in our instance- specific label smoothing, we propose a sample- aware prior distribution to smooth the labels. Specifically, we replace the fixed distribution with dynamic and informative distribution that is adaptively generated by the classification model itself. In practice, similar to Eq. 4, we smooth the label by interpolating the original label y4 with a p,y) predicted by the classification model. Over all the candidate classes yi a one-hot vector, where its value (i.e. probability) on the correct class is I and its value on the other class is O. p(y(æ;)) is the model's predicted probability distribution over all the classes. We consider the model prediction p(y) as the possibility of being the correct label from the model's perspective. As the model is opti- mized, the model prediction becomes increasingly accurate and the model predicted label approaches the ideal label. We obtain the final smoothed label y', as:
We use the representative BERT (Devlin et al., 2019)-BASE and -LARGE models as the backbone PLMs, and fine-tune them in a two-stage manner. Specifically, following many previous mixup meth- ods (Chen et al., 2020; Yoon et al., 2021), we first train the backbone PLMs (without using mixup) with a learning rate of 5e-5, and then continue fine- tuning the models using the mixup strategy with a learning rate of 1e-5. Note that our methods are only adopted in the second stage.
We set a maximum sequence length of 128 and a batch size of 32. AdamW (Loshchilov and Hutter, 2018) optimizer with a weight decay of le-4 is used to optimize the model. We use linear scheduler with a warmup for 10% of the total training step.
4.3 Compared methods
We compare our method with other cutting-eds counterparts. Specifically, taking the TMix (Chen et al., 2020) as the base mixup method, we use the following strategies to improve its performance:
- (1- + ar:
(5)
AUM (Park and Caragea, 2022): AUM com- pares logits to classify samples into two sets and then interpolates samples between these
Then we get the mixed smooth label 1% through the Eq. 2.

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SST2
RTE
MRPC
CB
Rott.
SUBJ
Amazon
Score
Method
Acc.
Acc.
Acc.
Acc.
Acc.
Acc.
Acc.
Avg.
A (t)
Performance of Different No Mixup Methods
BERT-base
55.43
49.59
53.67
32.49
59.46
82.72
56.46
55.68
-w/ EDA
54.72
49.80
59.20
39.60
58.96
82.06
62.2
58.07
+2.39
-w/ Back Translation
56.85
49.20
61.60
38,94
60.67
83.16
66.63
59.57
+3.89
-w/ CBERT
54.90
49.80
57.20
32.85
60.03
82.49
62.82
57.15
+1.47
Performance of Different TMix Improvement Methods
TMix
54.94
49.60
61.90
41.06
56,95
83.16
58.14
57.95
-w/ AUM
56.60
49.81
62.10
42.35
58.94
83.30
65.22
59.75
+1.80
-w/ DMix
53.68
54.40
46.40
56.80
41.80
51.76
88.66
56.21
-1.74
-w/ SE (Ours)
57.56
49.99
62.69
42.85
58.23
83.87
68.58
60.53
+2.58
Performance upon Different Mixup Methods
SSMix
55.70
49.52
60.10
37.13
59.86
83.76
62.63
58.08
-w/ SE (Ours)
56.96
49.96
61.41
39.63
61.27
84.06
65.60
59.83
+1.45
EMbedMix
53.11
49.52
61.61
37.49
58.83
83.10
63.34
58.14
-w/ SE (Ours)
55.89
49.88
63.28
41.07
60.10
83.86
69.22
60.46
+2.32
TreeMix
55.70
49.52
60.04
37.13"
559.8
83376
62.63
58.37
-w/ SE (Ours)
56.96
49.96
61.17
39.63
61.27
84.06
65.60
59.80
+1.43
Table Comparison between our SE and the vanilla method applied to mixup methods on the benchmarks. "Rott." is the short for Rotten tomato task. "A" denotes the improvement of SE methods compared to the baselines.
Model
CB
RTE
Rott.
Avg.
A Art)
Learning Strategy
SST2
Rott.
Amazon
Avg.
Raseline
37.84
48.51
58.55
48.30
Random
55.70
59.86
60. 12
58.56
-w/SSMii
42.49
48.37
599.67
50.17
"+1.7"
Easy-to-hard
55.81
61.17
65.37
60.78
-w/ SE (Ours)
47.49
49.16
62.26
52.97
+4.67
Hard-to-easy
55.79
61.13
64.64
60.52
Experimental results of comparison with BERTT-arreeAA values are average accuracy (%) of five runs with different seeds. Models are trained with 10 labeled data per class.
Table 2:
Table 3:
Experimental results of different data selec- tion methods. All values are average accuracy (%) of five runs with different seeds. Models are trained with 10 labeled data per class.
sets by identifying the most similar and most dissimilar samples from the opposite set.
EmbedMix (Guo et al., 2019), SSMix (Yoon et al., 2021) and TreeMix (Zhang et al., 2022).
DMix (Sawhney et al., 2022): DMix chooses samples based on their diversity in the embed- ding space.
4.4 Main Results
The full results of BERT-BASE and -LARGE are shown in Table and Table 2, and we can find that:
SE (Ours): SE divides the dataset into easy-to- learn and hard-to-learn and then mixes up two samples according the order of difficulty from easy to hard.
SE surpasses the cutting-edge counterparts in most settings. When using the TMix as base method, our SE brings much better performance improvements compared to the other counterparts (AUM and DMix), i.e., up +4.51 average score. Additionally, compared the other traditional DA methods, SE can also achieve superior performance. These results show the effectiveness our SE method.
Additionally, for reference, report the results of some traditional data augmentation methods, i.e. EDA (Wei and Zou, 2019) Back Transla- tion (Shleifer, 2019) and CBERT (Wu et al., 2019). To verify the universality of our SE, we also at- tempt to adopt it to other base mixup methods, ie..

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84.5
BERT-base
SSMIX
SSMIX-SE
Amazon
MRPC
98.0
90
84.0
accuracy(ss
Accuracy
96.5
Accuracy
86
83.5
95.0
78
83.0
93.5
74
82.5
70
70
100
Data Usage (%)
Data Usage(%)
82.
0.0
0.2
0.4
0.6
0.8
1.0
Figure 3: Results at various training data sampling rates. BERT-base models fine-tuned on Amazon and MRPC used. We can that our method achieves better performance across all data size regimes, espe- cially in the few-shot scenarios.
Figure 2: Parameter analysis of on BaRe-base, fine- tuned on SUBJ task.
Method
SST2
RTE
Amazon
Avg.
(t)
SSMix
55,81
49.73
65.37
56,97
56.12
49.81
63.11
57.01
+0.04
94.0
Roften - SSI2
48.0
Rotten
RTE
IW/ (Ours)
56.88
49.96
65.52
57.45
+0.48
Awturacy (1)
47.5
Table Experimental results of different label smooth- ing. All values are average accuracy (%) of five runs with different seeds. Models are trained with 10 labeled data per class.
93.5
47.0
S5MIK+3E
SEMIX+SE
MRPC
54.5
51.5
Acruraty (%)
Accennvv (96)
50.5
SE brings consistent and significant perfor- mance gains among all baselines. addition to the TMix, we also adopt our SE to more base mixup methods, i..., SSMix, EmbedMix and TreeMix, and show the contrastive results in Table 1. As seen, compared to the baselines, our SE can bring consis- tent and significant performance gains among all these methods, indicating its universality.
53.5
49.5
BERI-bese SSMix 55Mix+ SL
ULRI-base
SSMix 55Mix+5E
Figure Analysis of task generalization. The model is fine-tuned on the Rotten tomato task and transferred to four different tasks. We can see that our SE method consistently brings better generalization compared with its counterparts.
SE works well in both model sizes. Here, we verify whether our SE can still work in the large model scenarios. Taking some tasks as examples, we show the contrastive results in Table 2. It can be seen that, with the help of our SE, BERT-large achieves much better performance against the base- lines. These results prove the effectiveness of our SE in both model sizes.
iments as following: 1) "Random": we randomly select the samples from full dataset; 2) "Easy-to- hard": we first train the model with easy samples and then with hard samples; 2) "Hard-to-easy" the opposite learning order "Easy-to-hard", The de- tailed results are listed in Table 3, and we can find that both ordered learning strategies outperform the baseline "Random". indicating the significance of evolution learning. More specifically, "Easy-to- hard" achieves the best performance, thus leaving it as the default setting.
4.5 Ablation Studies
We evaluate the impact of each component of our SE, including i) learning strategy on mixup, ii) instance-specific label smoothing approach, fii) co- efficient a.
Comparison of Different Label Smoothing. A key technology in our method is the instance- specific label smoothing method. To verify its effectiveness,
Impact of Learning Strategy Mixup. As mentioned in $3.3, we perform the mixup process in easy-to-hard manner, i.e., first mixing the easy samples and then mixing the hard samples. Here, to investigate the impact of different learning strategies on mixup, we conduct contrastive exper-
compare it with vanilla label smoothing and report the results in Table 4. We show that 1) both label smoothing methods achieve better performance compared to the base- line, confirming the necessity to alleviate the over-

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confidence problem: 2) our method could further improve the results by a clear margin against vanilla label smoothing. These results prove the effective- ness of our ILS method.
our method boosts the performance of models on OOD data.
Visualization of Loss Landscape. To have a closer look, we also visualize the loss landscapes of different BERT-base models fine-tuned on the Rotten Tomato task. In practice, we follow the "filter normalized" setting in Li et al. (2018) and show the 3D loss surface results in Figure 5. We can that our method has flatter smoother sur- faacss compared to others. This result proves that SE can smooth the loss landscape and improve the generalization of models effectively.
Impact of Coefficient a. The weight a in Eq. is used to control the ratio of label smoothing, which is an important hyper-parameter. In this part, we examine its impact by evaluating the per- formance with different a SUBJ task, and illus- trate the results in Figure 2. As shown, compared with the baseline, our method consistently achieves better performance across all ratios a. More specifically, the case of a = 0.1 performs best, and we hereby use the setting in our experiments.
Conclusion
In this paper, propose a simple- eeffeffective self-evolution (SE) learning mechanism to improve the existing mixup methods on text classification tasks. SE for mixup follows two stages: conducting data division based on the degree of difficulty and mixup based on the order from easy to hard. SE can be used in various mixup methods to generate more adaptive and model-friendly pseudo samples for model training. Also, to avoid over-confidence in the model, propose novel cestance-specifee label smoothing approach. Extensive experiments on four popular mixup methods, EmbedMix, TMix, SSMix, and TreeMix, verify the effectiveness of our method. Quantitative analyses and in-depth discussions show our method improves the gener- alization, and robustness of models.
4.6 Expanding to High-resource Scenarios
Although our work mainly focuses on the data aug- mentation in few-shot tasks, we also investigate whether our method still works in the high-resource scenarios. Specifically,
change the percentage of training data used from 20% to 100% and illus- trate the results of several tasks Figure
As expected, our method achieves significant performance improvements when the amount of training data was extremely limited, continuing to confirm the effectiveness of our method. Moreover, we can also observe performance gains brought by our SE in the other relatively high-resource sce- narios. These results prove the universality of our method.
4.7 Analysis of Model Generalization
Limitations
To investigate whether our SE can bring better model generalization, we conduct experiments from two perspectives: i) measuring the cross-task zero-shot performance, and ii) visualizing the loss landscapes of models.
Our work has several potential limitations. First, due to limited computational resources, we only validate our self-evolution lcarning on base- and large-size BERT models. Expanding our experi- ments to larger model sizes would make our work more convincing. On the other hand, for the results of baseline methods, should compare results with those in the original paper for fair compari- son. However, due to the difference of PLMs and tasks used in the other baselines and ours, it is un- reasonable to compare the results directly. Hence. as an alternative, we only reproduce the results our settings using the code in the corresponding papers.
Task Generalization. The performance of out- of-domain (OOD) data is widely used to verify the model generalization (Xu al., 2021; Zhong et al., 2022). Hence, we follow Zhong al. (2022) and and evaluate the performance of models on several OOD data. In practice, we first fine-tune BERT- based models trained with different methods (in- cluding "Baseline". "SSMiX", and "SSMix+SE") on the Rotten Tomato task, and then inference on other tasks, i..,, SST2, MRPC, RTE, and Ama- zon. The results are illustrated in Figure 4. We ob- serve that SES consistently outperforms the other counterparts. To be more specific, com- pared with baseline, our SE brings +0.47 average improvement score on these tasks, indicating that
Ethics Statement
We take ethical considerations very seriously, and strictly adhere to the EMNLP Ethics Policy. This paper proposes a self-evolution learning algorithm

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9.44
03
037
630
6.
0.68
1.3
1.00
0.66
BERT-base
SSMix
SSMix+
Figure 5: The 3D loss surface comparison between baseline, vanilla SSMix, and our SE methods applied to BERT- base. Note that the PLMS fine-tuned on the Rotten tomato task. It can be that SE methods significantly smooth the loss surface, i.c., improving the model generalization effectively.
improve the existing mixup strategy. The pro- posed approach aims precisely augment the few- shot training data with the original training cor- pus, instead of encouraging the model to gener- ate new sentences that may cause the ethical prob- lem. Morcover, all pre-trained language models and downstream datasets used in this paper are publicly available and have been widely adopted by researchers. Thus, we believe that this research will not pose ethical issues.
Yu Cao, Liang Ding, Zhiliang Tian, and Meng Fang. 2021. Towards efficiently diversifying di- alogue generation via embedding augmentation. In ICASSP 2021-2021 IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP), pages 7443-7447, IEEE.
Jiaao Chen, Zichao Yang. and Diyi Yang. 2020. Mix- Text: Linguistically-inowr
den space for cemi-supervised interpolation of hid- classification. Proceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 2147- 2157, Online. Association for Computational Lin- guistics.
Acknowledgements
Jacob Devlin, Ming-War Wei Chang. Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Lan- guage Understanding. arXiv e-prints, page arXiv:1810.04880
This work is supported by the following foun- dations: the National Natural Science Founda- tion of China under Grant No.62025208 and No.62306330 the Xiangjiang Laboratory Foun- dation under Grant No.22XJ01012.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre- fre-training of deep bidirectional transformers for language under- standing. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Technologies, Volume (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.
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analyze the influence of different À in detail. In practice, we used TMix as the baseline and con- ducted experiments with À varying from 0.4. The results are presented in the table 6. As seen, our method brings consistent performance gains across various, indicating that our method is not sensitive to the value of X. Notably, "A=0,2" achieves the best performance, thus leaving the default setting.
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Appendix
A.1
Details of Datasets
Dataset
Task
# Label
Size
SST-2
Sentiment
2
67k 1.8k
RTE
NLI
2
2.5k 33
MRPC
Paraphrasc
2
3.7k 1.7k
556 250
SUBJ
Classification
2
8k / 2k
Rotten tomato
Scatiment
2
8.53k/ 1.07k
Amazon counterfactual
Classification
2
5k 5kkk
Table 5: Dataset name, task, number of total labels, and dataset size datasets we used henchmark. The task column describes the objective of each dataset.
A.2 Parameter Analysis
As stated in Sec3.2, We use the parameter to control the mixing ratio of two samples. Here, we
0.2
0.3
TMix
$5.01
54.94
54.46
54.93
Ours. 55.66(+0.65)
57.56(4262)
54.9340.47)
45.40(+0.4)
Table The experimental results were an average of 5 runs with random seeds. The content in the brackets is the improvement our method.


Reviews:
[{"review_id": "bBFQwCFJ7Y", "reviewer": "Reviewer_hra8", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive and valuable comments! Following your suggestions, we are happy to clarify our concerns and refine our paper.\n\n### R1:  didn't directly compare the **results with those in the original paper**\n\nIn this paper, we primarily consider the problem of data augmentation **in the few-shot setting**. Therefore, we designed extensive experiments in few-shot scenarios to validate the performance of the proposed method. However, the results in the original paper are mainly based on the **full dataset (instead of the few-shot setting)**.\n\nWe also recognize the relevance of comparing to the original dataset. In Figure 3 of our paper submission, we investigated the performance of our method with different amounts of data, including a comparison of model performance on some datasets with full data sets.\nWe have released our code, which ensures other researchers can reproduce our results in the few-shot setting.", "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": "The strengths of this paper include its novel approach to data augmentation in text classification, its comprehensive evaluation on various benchmark datasets, and its instance-specific label smoothing regularization approach that alleviates the over-confidence problem in few-shot text classification tasks. This paper has the potential to make a significant contribution to the NLP community by addressing an important problem in few-shot text classification and providing a novel and effective solution that can be applied to other NLP tasks as well.", "reasons_to_reject": "For the results of baseline methods, the authors didn't directly compare the results with those in the original paper, due to the difference of PLMs and tasks used in the other baselines and ours. As an alternative, the authors only reproduce the results in their settings using the code in the corresponding papers. There may remain a risk on validating the true contribution/improvement of this approach.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a new approach to data augmentation in text classification called Self-Evolution Learning for Mixup. The main problem addressed in this paper is the over-confidence problem in few-shot text classification tasks, where the model tends to overfit to the limited training data and perform poorly on unseen data. \nThe main contributions of this paper are:\n1. A new data augmentation approach called Self-Evolution Learning for Mixup that generates more adaptive and model-friendly pseudo samples.\n2. An instance-specific label smoothing regularization approach that alleviates the over-confidence problem in few-shot text classification tasks.\n3. A comprehensive evaluation of the proposed method on various benchmark datasets, demonstrating its effectiveness and superiority over existing methods. \nOverall, this paper provides a novel and effective solution to the over-confidence problem in few-shot text classification tasks, which has important implications for real-world applications of natural language processing.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "OWSIAPG3iS", "reviewer": "Reviewer_g5MQ", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful and positive comments! We follow your suggestions and we believe we have solved all your concerns as follows! \n\n### R1: make the best results in bold\n\nWe will follow your suggestions to bold the results in our next version.\n\n### R2: run each experiment for 5 times and report means and variation\n\nThanks! In fact, the results reported in our paper were already the mean is the average of five random seed runs (mentioned in **line 357** of the paper), and we will include the variation results in the next version of the paper. The partial results are shown in the table below. \n\n| **Method**          | **SST2**   | **RTE**    | **MRPC**   |\n|-----------------|--------|--------|--------|\n| SSMix(baseline) | 55.70% | 49.52% | 60.10% |\n| Ours            | 56.96% | 49.96% | 61.41% |\n| Difference      | +1.26% | +0.44% | +1.31% |\n| Varience        | 0.47%  | 0.15%  | 0.69%  |\n\n### R3: no code was provided to help in checking the specific details\n\nWe have promised that we would release our code after the paper's acception.  At the rebuttal stage, we **release our code** in an anonymous link: https://anonymous.4open.science/r/Self-evolution-learning-for-mixup-4F8D/\n\n### R4: hard-to-easy achieves very similar results to easy-to-hard\n\nRegarding the issue of the gap between \"easy-to-hard\" and \"hard-to-easy\" in Table 3, we can refer to the average improvement effect of the mixup method in other papers. \n\nThe average accuracy improvement of the Mixup-Transformer [1]  method in line 8  of Table 1 in their paper is 0. 56% and the SSMix [2]  method has an average improvement effect of 0.4% on the GLUE dataset compared to the baseline, and the difference between  \"easy-to-hard\" and \"hard-to-easy\"  is 0.2%, which is in the same order of magnitude. Therefore, the improvement margin of our method is sufficient enough to distinguish two of our variants in ablation studies. \n\n[1] Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks.(Sun, Lichao, et al., COLING2020)\n\n[2] SSMix: Saliency-Based Span Mixup for Text Classification (Yoon et al., ACLFindings 2021)\n\n### R5：missing References: Sahu, G., et al. \"Data augmentation for intent classification with off-the-shelf large language models. CoRR (2022).\"\nWe fully agree with your comment regarding the missing reference, and we will include it in our paper.", "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": "- The paper is well written and easy to read, I like Figure 1in the way it shows the pipeline of the method and Algorithm 1 in the way it shows more specific control flow of the data.\n\n- The authors did a great job at testing their SE method on a wide variety of benchmarks showing consistent improvements and illustrating that that the division of the samples to easy/difficult splits is a promising strategy that goes well with mixup.\n\n- The authors made extensive ablation studies that shows the efficacy of the proposed method and the robustness of the hyperparameters such as the alpha of BERT-Base\n\n ", "reasons_to_reject": "- I encourage the authors to make the best results in bold to make the experiment tables easier to parse.\n\n- I encourage the authors to run each experiment 5 times and report the mean and variance to see the significance of the records\n\n- No code was provided to help in checking the specific details of the pipeline and reproduce the results\n\n- It looks like from Table 3 that hard-to-easy achieves very similar results to easy-to-hard. This result makes me wonder whether the \"easy\"/\"hard\" strategy is what makes this method outperform others or whether there are additional components that this method has that makes it SOTA. This phenomenon needs to be explained.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper presents a method called self-evolution learning (SE) for better mixup data augmentation in text classification tasks. The authors split the text data into two sets: easy and difficult samples with the help of BERT. They then apply a method called mixup, starting with the easy set and gradually moving to the difficult ones. To prevent the model from being overconfident in its predictions, they use a method to create soft labels based on the original hard labels. This approach enhances the model's ability to handle different types of text and perform better on text classification tasks, as demonstrated through extensive experiments.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Sahu, G., et al. \"Data augmentation for intent classification with off-the-shelf large language models. CoRR (2022).\""}, "extra_text": {}}, {"review_id": "kogkJQT5ei", "reviewer": "Reviewer_YbFy", "summary": "", "questions": "1. The improvements observed in the ablation experiments are marginal, and the significance of these results is unclear. Can you perform statistical tests, such as a t-test, to validate the effectiveness of the proposed \"instance-specific label smoothing\" method? How do you respond to the observed minimal differences in accuracy, and what do they indicate about the practical applicability of your approach?\n\n2. Your experimental design appears limited in scope, particularly concerning the analysis of hyperparameters and the range of models used for comparison. Could you provide more insights into the choice of hyperparameters and why some were not analyzed experimentally? Would you consider conducting experiments with other models such as Bart, T5, RoBERTa, to demonstrate the generality of your proposed method, and how do you envision these additional experiments strengthening or altering the conclusions of the paper?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Motivated by your comments, we have deeply reconsidered the architecture of our work and tried to fix all the problems you mentioned.\n\n### R1: The innovation of this paper is limited.\n\nAs stated Our work not only proposes a **new mixup training strategy** but also proposes a **dynamic label smooth** method: instance-specific label smooth.  Our proposed method caters to the **growth of the model learning ability** and adapts to the ability when generating training samples. \n\nMoreover, compared to other mixup methods, the method we propose is not customized for specific scenarios and have the potential to be applied to various mixup methods for improvement.\n\n\n### R2:· The accuracy difference between Easy-to-hard and Hard-to-easy is only 0.2% and the \"instance-specific label smoothing\" method demonstrates a marginal improvement of 0.48%\n\nCompared with the mixup method proposed in other papers, the average improvement of the baseline, SSMix [1] has an average improvement of about 0.4% compared with the baseline in the GLUE dataset and the Mixup-transformer has an average improvement of about 0.56% in line 8 of Table 1 in their paper. The improvement of 0.48% (line 4 in Table 2) achieved by the proposed label-smooth method is higher than 0.4%. Moreover, the interpolation techniques of 0.2% Eassy-to-hard and hard-to-easy (Table 3) are in the same order of magnitude as 0.4% and 0.56%.  Therefore, the improvement margin of our method is sufficient compared to other papers in this field.\n\n\n### R3: some errors in the sentence\n\nThank you for your detailed inspection and we apologize for our negligence. We have made revisions to these errors in the paper as follows:\n\n(1)In line 275,  we have modified \"hard-to-learn\"  to \"easy-to-learn\".\n\n(2)In line 293,  we have modified the punctuation before 'The'.\n\n(3)In line 286, we re-write the influent sentence as \"We mix the two samples by interpolating the inputs and labels as Eq.1 and Eq.2. The data selected according to the above process is then used for training\".\n\n### R4: the hyperparameter $\\lambda$ of the mixup method is not analyzed experimentally.\n\nFor your suggested experimental analysis of the λ parameter, at the rebuttal stage, we have conducted the experiment and analyzed the effectiveness of our method with **different values of λ**. We used TMix as the baseline and conducted experiments with λ varying from 0.1 to 0.4. The experimental results were an average of 5 runs with random seeds. The results are presented in the following table. The content in the brackets is the improvement of our method.  The content in the brackets is the improvement of our method.\n\n\n| $\\lambda$         | 0.1          | 0.2          | 0.3          | 0.4          |\n|------------|--------------|--------------|--------------|--------------|\n| TMix       | 55.01        | 54.94        | 54.46        | 54.93        |\n| TMix+Ours. | 55.66(+0.65) | 57.56(+2.62) | 54.93(+0.47) | 55.40(+0.47) |\n\nAs seen, our method brings consistent performance gains across various $\\lambda$, indicating that our method is not sensitive to the value of $\\lambda$. Notably, \"λ=0.2\" achieves the best performance, thus leaving as the default setting.\n\n### R5: Can you perform statistical tests to validate the effectiveness of the proposed \"instance-specific label smoothing\" method? and  respond to the observed minimal differences in accuracy\n\nSure, for the statistical tests, we follow the suggestion of reviewer 2 (g5MQ): conducting each experiment five times and calculating the mean and variance of the results. The results are shown in the table below.\n\n|                 | SST2   | RTE    | Amazon |\n|-----------------|--------|--------|--------|\n| SSMix(baseline) | 55.81% | 49.73% | 65.37% |\n| SSMix+ILS(ours) | 56.88% | 49.96% | 65.52% |\n| Difference      | +1.07% | +0.23% | +0.18% |\n| Varience        | 0.31%  | 0.03%  | 0.02%  |\n\nFrom the variance in the table, it can be seen that the ILS we proposed has a stable improvement and is effective. \nFor the issue of observed minimal differences in accuracy, please refer to the response to R2.\n\n### R6: Provide more insights into the choice of hyperparameters, why some were not analyzed experimentally \nand would you consider conducting experiments with other models such as Bart, T5, RoBERTa\n\nBefore submission, we conducted experiments and found that the best performance was obtained when a was set to 0.1. The following table presents the results obtained in that experiment. \n\n| α    | 0.1   | 0.2  | 0.3   | 0.4   | 0.5  | 0.6  | 0.7   | 0.8   | 0.9  |\n|------|-------|------|-------|-------|------|------|-------|-------|------|\n| Οurs | 84.06 | 82.9 | 82.84 | 82.89 | 82.9 | 82.9 | 82.83 | 82.86 | 82.9 |\n\nFor mixup, we used the mixing proportion settings as described in the original mixup papers [1,2,3,4].  To avoid unfair comparison due to tuning parameters, we used the default hyperparameter values consistent with the baseline.  Now we have conducted experiments on the mixup hyperparameter λ, and the results are shown in the table below.      \n\n| λ          | 0.1          | 0.2          | 0.3          | 0.4          |\n|------------|--------------|--------------|--------------|--------------|\n| TMix       | 55.01        | 54.94        | 54.46        | 54.93        |\n| TMix+Ours. | 55.66(+0.65) | 57.56(+2.62) | 54.93(+0.47) | 55.40(+0.47) |\n\nAdditionally, for hidden-level mixup methods such as TMix, the number of mixed layers was also set to the default values.We will add more parameter analyses and experimental details in the revised paper.\n\nSure, we are willing to conduct experiments on other pre-trained models. Due to time constraints, we did not conduct these experiments during the rebuttal stage, but we will verify our method on the T5 and Roberta models and include the results in our paper.\n\n\n[1] SSMix: Saliency-Based Span Mixup for Text Classification (Yoon et al., ACLFindings 2021)\n\n[2] Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks.(Sun, Lichao, et al., COLING2020)\n\n[3] DMix: Adaptive Distance-aware Interpolative Mixup (Sawhney et al., ACL 2022)\n\n[4]MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (Chen et al., ACL 2020)", "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": "1.The paper proposes a simple and explainable data augmentation method.\n\n2.The diagrams illustrating the model structure are clear and concise. \n\n3.The writing is overall good.", "reasons_to_reject": "1.This paper appears to simply divide the dataset based on the learning difficulty of the samples and apply the mixup methods, without presenting any particularly innovative aspects. I think that the innovation of this paper is limited.\n\n2.The credibility of the ablation experiments in this paper is insufficient. Among them, the ablation experiments for \"different data selection methods\" show that the accuracy difference between Easy-to-hard and Hard-to-easy is only 0.2%. However, the ablation experiments for the proposed \"instance-specific label smoothing\" method demonstrate a marginal improvement of 0.48% (less than 1%) compared to the baseline, which does not sufficiently indicate the effectiveness of this method and requires a t-test for further validation.\n\n3.There are some errors in the paper, mainly concentrated on the fourth page of the paper.\n\n1)In the sentence \"we assign samples with a degree of difficulty less than the median to the hard-to-learn dataset \", \"hard-to-learn\" should be modified to \"easy-to-learn\".\n\n2) In the sentence \",The sample serves as a new sample to augment the training data.\", the punctuation before \"The\" should be modified to a period.\n\n3) The sentence \"we mix the two samples up by interpolating the inputs ( and ) and labels ( and ) as Eq.1 and Eq.2. The data selected according to the above process is then used for the mixup\" is repetitive and not fluent, and needs to be revised.\n\n4. The paper lacks some experiments. Regarding hyperparameters, this paper only conducts experimental analysis on the parameter α of instance-specific label smoothing, while the hyperparameter λ of the mixup method is not analyzed experimentally. Additionally, this paper only compares with BERT-base and BERT-large in the experimental setup. I believe it is necessary to conduct experiments with more models, such as Bart, T5, RoBERTa and others, to validate the generality of the proposed method in this paper.", "questions_to_authors": "1. The improvements observed in the ablation experiments are marginal, and the significance of these results is unclear. Can you perform statistical tests, such as a t-test, to validate the effectiveness of the proposed \"instance-specific label smoothing\" method? How do you respond to the observed minimal differences in accuracy, and what do they indicate about the practical applicability of your approach?\n\n2. Your experimental design appears limited in scope, particularly concerning the analysis of hyperparameters and the range of models used for comparison. Could you provide more insights into the choice of hyperparameters and why some were not analyzed experimentally? Would you consider conducting experiments with other models such as Bart, T5, RoBERTa, to demonstrate the generality of your proposed method, and how do you envision these additional experiments strengthening or altering the conclusions of the paper?", "paper_topic_and_main_contributions": "This paper proposes a self-evolution learning-based mixup data augmentation method to address the problem of data scarcity in few-shot text classification scenarios. \n\nThe method is primarily divided into two stages: conducting data division based on the degree of difficulty and performing mixup in the order from easy to hard.To further alleviate the model over-confidence, this paper also proposes an instance-specific label smoothing regularization approach which linearly interpolates  the model's output and one-hot labels of the original samples to generate new soft labels for label mixing up.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "K22SQK7y2Z", "reviewer": "Reviewer_RCEN", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful review! Here we address your questions about the work. \n\n### R1: Lack comparison with other fine-tuning method.\n\nWe will compare some other methods, but due to time constraints, now we only compared Text smoothing [1]The comparison results are shown in the table below. \n\n| Method         | TREC           |\n|----------------|----------------|\n| Text Smoothing | 67.51%         |\n| Ours           | 68.13%(+0.62%) |\n\nBased on the results presented in the table, it can be observed that our method has an advantage over the other methods compared.\n\n### R2: extent over-confidence can be partially solved by label smoothing is not shown in this paper\n\nLabel smoothing **breaks the model's excessive dependence on specific labels** by introducing uncertain labels during training. Each sample's label is modified into a probability distribution instead of a rigid 0 or 1. For example, in a binary classification problem, the label may be set as 0.9 (positive class) and 0.1 (negative class), instead of simply 0 or 1. This way, the model needs to learn from a probability distribution instead of just learning a hard label. Intuitively, **estimating a distribution can be seen as studying from the target with a confidence** score (the probability acts as the confidence score ), but estimating a hard label can be seen as learning from a target with 100% confidence, thus leading to over-confidence.\n\nWe **conducted an experiment** to calculate the statistical loss for the samples with and **without instance-specific label smoothing (ILS). And we compared their corresponding variances. The results are shown in the following table, which demonstrates that the sample loss with ILS is more stable. \n\n\n\n|       | variance |\n|-------|----------|\n| without ILS | 0.039    |\n| with ILS    | 0.013    |\n\n### R3:  the introductions of line51-61 makes this paragraph hard to convey information to audiences who are not familiar with data augmentation,\n\nWe will describe the DA method in a more accessible way, as described below.: DA methods based on synthesis such as mixup and others like back-translation and EDA.\n\n\n### R4: The Figure 1 is hard to read and adding a figure to display how equation (5) evolves,\n\nWe commit to providing the changes in the next version. In the rebuttal stage, we give the idea of the figure.  We use **a test sample**: \"take on loss and loneliness\"  , One-hot label [1 0] of this sample is transformed into [0.73 0.27] after label smoothing. We use a **histogram to represent the label distribution**. This allows us to directly observe the changes in the labels. Additionally, we will show the corresponding changes in the sample's loss.\n\n\n\n### R5: What makes us link loss surface smoothness in Figure 5 and the proposed method?\n\nThe smoothness of the loss surface can intuitively reflect the **generalization performance** of a model [2,3]and reflect the over-confidence issue is relieved. When the loss surface is smooth, it indicates that the model can adapt well to small perturbations in the input, and the loss value does not change significantly. \n\nWe mentioned before that our approach would cause an over-confidence problem, so we tried to introduce an instance-specific label smoothing method. And now we have proven that our approach can indeed improve generalization (smoother loss landscape and better ood effect), confirming our claim. \n\n\n[1] Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks] (Wu et al., ACL 2022). \n\n[2] Visualizing the Loss Landscape of Neural Nets\n\n[3] Sharpness-Aware Minimization for Efficiently Improving Generalization", "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": "1. The instance-specific label smoothing method is straightforward and effective.\n2. Better experimental results than other mixup counterparts.", "reasons_to_reject": "1. Lack comparison with other fine-tuning methods.\n2. The overconfidence issue is a common problem of overparameterized models like pretrained language models, to what extent this problem can be partially solved by label smoothing is not shown in this paper. Since this is the key hypothesis of this paper, experimental analysis about the prediction logits distribution with/without the proposed label-smoothing method should be available. \n3. Line 51 - 66 introduces data augmentation from the angle of changing to the label, it makes this paragraph hard to convey information to audiences who are not familiar with data augmentation, though it greatly fits the topic of this paper. A better way might be DA methods based on synthesis such as mixup and others like back-translation and EDA.\n4. The figure 1 is hard to read and adding a figure to display how equation (5) evolves, by referring to models performance in test data, would be much more inspiring since audiences would spend more time on understanding the content of section 3.4.\n5. What makes us to link loss surface smoothness in figure 5 and the propose method?", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper introduces a mixup-based data augmentation method for improving few-shot text classification with pretrained language models. Specifically, the authors leverage a curriculum strategy to effectively employ training data and propose a label smoothing method that can fit the curriculum learning strategy.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This work studies mixup for few-shot text classification and proposes a 2-stage adaptation using two different subsets of the data. Reviewers in general agree about the experimental rigor of this work and found it novel and well-written. There are some concerns about the limited improvements observed. Though a SOTA result with big improvement is not a requirement for acceptance, it reduces the excitement felt by the reviewers.
Decision: Accept-Main