Are Compressed Language Models Less Subgroup Robust?
Authors: Leonidas Gee
Andrea Zugarini
Novi Quadrianto
Venue: EMNLP 2023 Main (2023)
Keywords: Language Model Compression
Subgroup Robustness

Abstract: To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup robustness of BERT language models. We show that worst-group performance does not depend on model size alone, but also on the compression method used. Additionally, we find that model compression does not always worsen the performance on minority subgroups. Altogether, our analysis serves to further research into the subgroup robustness of model compression.

Are Compressed Language Models Less Subgroup Robust?
Leonidas Geel
Andrea Zugarini* Novi Quadrianto 1,2.
Prrddictive Analytics Lab, University of Sussex, UK
BBAAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning, Spain Monash University, Indonesia expert.ai, Siena, Italy
Model
Size (MB)
Parameters
Abstract
BERT Bose
438.01
109M
BERT Medium
165.55
41M
To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. How- ever, little is known about their robustness to minority subgroups defined by the labels and attributes of dataset. In this paper, inves- tigate the effects of 18 different compression methods and settings on the subgroup robust- ness of BERT language models. We show that worst-group performance does not depend on model size alone, but also on the compression method used. Additionally, we find that model compression does not always worsen the per- formance on minority subgroups. Altogether, our analysis serves to further research into the subgroup robustness of model compression.
BERT Small
115.09
29M
BERT Mini
44.71
11M
BERT
17.56
4M
DistilBERT
267.86
67M
TinyBERT,
267.87
67M
TinyBERT
57.43
14M
BERT
415.97
104M
PERT
393.14
98M
BERT/100
370.31
93M
PRRT
347.48
87M
BERT DQ
181.48
24M
BERT SQ
182.89
24M
BERT QAT
182.89
24M
BERTvTI00
438.01
109M
BERT VT75
414.57
104M
1 Introduction
BERT V7500
391.13
98M
In recent years, the field of Natural Language Processing (NLP) has in the application of Large Language Models (LLMs) (Brown al., 2020; Thoppilan al., 2022; Touvron et al., 2023). These applications range from simple document classification to complex conversational chatbots. However, the uptake of LLMs has not been evenly distributed across soci- cty. Due to their large inference cost, only a few well-funded companies may afford to run LLMs at scale. To address this, many have turned to model compression create smaller language models (LMs) with near comparable performance to their larger counterparts.
BERT
367.69
92M
surge in interest
Table I: Model size and number of parameters. BERT is shown as haseline with subsequent models from knowledge distillation. structured pruning,
quantization, and vocabulary transfer respectively.
heads (Michel al., 2019) or embeddings (Gee et al., 2022) are essential. Although model com- pression has been proven to work well in practice, little is known about its influence on subgroup ro- bustness.
In any given dataset, subgroups exists as a com- bination of labels (e.g. hired or not hired) and attributes (e.g. male or female) (Sagawa et al., 2020; Bartlett et al., 2022). A model is said to be subgroup robust if it maximizes the low- performance across subgroups (Gardner et al., 2023). Due to the unbalanced sample size of each subgroup, the conventional approach to training via Empirical Risk Minimization (ERM) (Vapnik, 1999) produces models with a higher performance
The goal of model compression is to reduce a model's size and latency while retaining overall per- formance. Existing approaches such as knowledge distillation (Hinton et al., 2015) have produced scal- able task-agnostic models (Ture al., 2019; Sanh et al., 2020; Jiao et al., 2020). Meanwhile, other approaches have shown that all transformer
Corresponding author: ig7 177 ac.u.

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MukiNI
CiviCommmnts
SCOTUS
0.70
8.62
Accurre
0.55
Wire Seeee-gm--ii Aeruresss
0.50
0.30
0.43
0.49
0.29
Viree
0.10
0.23
0.52
0.32
Average Accuricy
dvittage Accuracy
Aierigg Accuracy
Dusiec
SCK5
Figure 1: Plot of WGA against average accuracy. Compression method is represented by marker type, while model size is represented by marker size. In MultiNLI and SCOTUS, compression worsens WGA for most models. Conversely, WGA improves for most compressed models CivilComments.
on majority subgroups (e.g. hired male), but a lower performance on minority subgroups (e.g. hired female).
to maintain overall performance. Hooker et al. (2020) followed up by analyzing how model compression affects the performance sensitive attributes of CelebA. Unitary attributes of gender and age as well their intersections (e.g. Young Male) were considered. The authors found that overall performance was preserved by sacrific- ing the performance on low-frequency attributes. Stoychev and Gunes (2022) expanded the pre- vious analysis on attributes to the fairness of fa- cial expression recognition. The authors found that compression does not always impact fairness in terms of gender, race, or age negatively. The impact of compression was also shown to be non- uniform across the different compression methods considered.
Given the increasing role of LLMs in everyday life, our work seeks address gap in the exist- ing literature regarding the subgroup robustness of model compression in NLP. To that end, we explore a wide range of compression methods (Knowledge Distillation, Pruning, Quantization, and Vocabu- lary Transfer) and settings on 3 textual datasets MultiNLI (Williams al., 2018), CivilCom- ments (Koh al., 2021), and SCOTUS (Chalkidis al., 2022). The code for our paper is publicly available
The remaining paper is organized as follows. First, we review related works in Section 2. Then, we describe the experiments and results in Sec- tions 3 and 4 respectively. Finally, we draw our conclusions in Section
To the best of our knowledge, we are the first to investigate the effects of model compression on subgroups in a NLP setting. Additionally, our analysis encompasses much wider range of com- pression methods than were considered in the afore- mentioned works.
2 Related Works
Most compression methods belong to one of the fol- lowing categories: Knowledge Distillation (Hinton et al., 2015), Pruning (Han et al., 2015), or Quan- tization (Jacob et al., 2017). Additionally, there exists orthogonal approaches specific to LMs such Vocabulary Transfer (Gee et al., 2022). Previous works looking at the effects of model compression have focused on the classes or attributes images. Hooker et al. (2021) analyzed the performance of compressed models on the imbalanced classes of CIFAR- 10, ImageNet, and CclebA. Magnitude pruning and post-training quantization were consid- ered with varying levels of sparsity and precision respectively. Model compression is found to canni- balize the performance small subset of classes
Experiments
The goal of learning is to find function f that maps inputs X to labels y E Y. Additionally, there exists attributes a E A that are only provided as annotations for evaluating the worst-group per- formance at test time. The subgroups can then be defined as E (Y x A).
3.1 Models
We utilize 18 different compression methods and settings on BERT (Devlin et al., 2019). An overview of each model's 's size and parameters is shown in Table
Knowledge Distillation (KD). We analyze seven models (BERT Mediamm
hhttps://githie http:p:///gggiuubb.m////arapppp compression-subgroup
BERT BERTsmalls

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MukiNU (0.
MuitiNu (0, 21
MuttiLI
0.85
6.70
0.85
0.60
0.82
0.50
Wore Srr-sggn--n
0.75
Voree
0.75
0.73
0.63
0.70
2.52
2:44
3.74 0.76
Awerage Accuricy
Avtragge Accuracy
Aieragg Acturacy
Dusieet
SCNS
Figure 2: Model performance is shown improve across the binary datasets of MultiNLI However, the overall trend in WGA remains relatively unchanged, with a decreasing model size leading to drops in WGA.
BERT Miné, BERTTing DistilBERT, TinyBERT 6. TinyBERT,) distilled from the uncased version of BERT Base using different distillation meth- ods (Turc et al., 2019; Sanh et al., 2020; Jiao et al., 2020). Each model is loaded from HuggingFace* with its pre-trained weights.
the datasets to SCOTUS from the FairLex benchmark (Chalkidis et al., 2022).
Further details regarding the subgroups in each dataset are shown in Appendix A.1.
Given hypothesis and premise, the task is to predict whether the hypothesis is con- tradicted by, entailed by, neutral with the premise (Williams al., 2018). Following Sagawa et al. (2020), the attribute indicates whether any negation words (nobody, no, never, or nothing) ap- pear in the hypothesis. We use the same dataset splits as Liu et al. (2021).
MultiNLI.
Pruning. We analyze structured pruning (Michel al., 2019) BERT following three-step train- ing pipeline (Han al., 2015). Four different levels of sparsity (BERT PS200- BERT PRA0: BERT PR600 BERT PRSO) are applied by sorting all transformer heads using the Il-norm of weights from the query, key, and value projection matrices. Structured prun- ing is implemented using the NNI library".
CivilComments.
Given online comment, the task to predict whether it is neutral or toxic (Koh et al., 2021). Following Koh al. (2021), the attribute indicates whether any demographic iden- tities (male, female, LGBTQ, Christian, Muslim, other religion, Black, or White) appear in the com- ment. We use the same dataset splits as Liu et al. (2021).
Quantization. We analyze 3 quantization meth- ods supported natively by PyTorch namic Quantization (BERT nQ). Static Quantiza- tion (BERTsQ) and Quantization-ave -rawt Training (BERTQAT) Quantization is applied to the linear layers of BERT to map representations from FP32 to INT8. The calibration required for BERT sQ and BERT QAT is done using the training set.
Dy-
SCOTUS. Given a court opinion from the US Supreme Court, the task is to predict its the- matic issuc area (Chalkidis et al., 2022). Fol- lowing Chalkidis et al. (2022), the attribute in- dicates the direction of the decision (liberal or conservative) as provided by the Supreme Court Database (SCDB). We use the same dataset splits as Chalkidis et al. (2022).
Vocabulary Transfer (VT). We analyze vo- cabulary transfer using different vocabu- lary sizes (BERTyTIO0. BERTyr7S, BERTyT50, BERT as done by Gee et al. (2022). Note that BERTyTION does not compress the LM, but adapts its vocabulary fully to the in-domain dataset, thus making tokenization more efficient.
3.2 Datasets
3.3 Implementation Details
Our analysis is done on 3 classification datasets. textual
We train each compressed model via ERM with 5 different random initializations. The average ac- curacy, worst-group accuracy (WGA), and model size are measured metrics. The final value of each metric is the average of all 5 initializations. Following Liu et al. (2021); Chalkidis et al. (2022), we fine-tume the models for 5 epochs on
MultiNLI and CivilComments datasets used by most subgroup robustness research (Sagawa et al., 2020; Liu et al., 2021; Izmailov et al., 2022). Additionally, we extend
Phttps://githir htwp://wwwooooteegnneeeeeerrerre htettps:/ggttuub.... wttp:/thww..commmcccrootttt/t//t

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MuitiNU
CivilComments
1.0
1.0
0.3
Accura
0.6
Accuracy
0.4
0.2
0.2-
0.0
0.0
[57499]] [11158]
[67376]
[1521]
(66630] [1992]
[148186]
[90337]
[12733]
(17784]
Subgroup
Subgroup
IBE Tinecuur
BERTSma
BERTray
Dinneer
ToyBER
TayBERT,
Figure 3: Distribution of accuracies by subgroup for KD. Sample sizes in the training set are shown beside each subgroup. In CivilComments, performance improves on minority subgroups (2 and 3) across most models as model size decreases contrary to the minority subgroups (3 and of MultiNI.I
MultiNLI and CivilComments and for 20 on SCO- TUS. A batch size of 32 is used for MultiNLI and 16 for CivilComments and SCOTUS. Each model is implemented with AdamW opti- mizer (Loshchilov and Hutter, 2019) and early stop- ping. A learning rate of 10-5 with no weight decay used for MultiNL.I, while learning rate of 10 with a weight decay of 0.01 is used for Civil- Comments and SCOTUS. Sequence lengths are set to 128, 300, and 512 for MultiNLI, CivilComments, and SCOTUS respectively.
than (BERT Mini BERT Tang. and TinyBERT, is shown to collapse to in SCOTUS.
Second, we further analyze compressed mod- els with similar sizes by pairing DistilBERT with TinyBERT, well post-training quantization (BERT no and BERT)Q) with BERTQAT accord- ing to their number of parameters in Table 1. We find that although two models may have an equal number of parameters (approximation er- ror), their difference in weight initialization af- ter compression (estimation error) as determined by the compression method used will lead to varying performance. In particular, DistilBERT displays lower WGA on MultiLII and Civil- Comments, but a higher WGA on SCOTUS than TinyBERT Additionally, post-training quanti- zation (BERT nQ and BERTsQ) which does not include an additional fine-tuning step after com- pression or a compression aware training like BERTQAT is shown to be generally less subgroup robust. These methods do not allow for the recov- ery of model performance after compression prepare for compression by learning compression- robust weights.
As done by Gee al. (2022), epoch of masked- laaguae modelling is applied before fine- tuning for VT. The hyperparameters are the same as those for muc-tuning except for a batch size 8.
4 Results
Model Size and Subgroup Robustness. We plot the overall results in Figure I and note a few in- teresting findings. First, in MultiNLI and SCO- TUS, we observe a trend of decreasing average and worst-group accuracies model size decreases. In particular, TinyBERT, appears to be outlier in MultiLII by outperforming every model including BERT Base- However, this trend does not hold in CivilComments. Instead, most compressed models show an improvement WGA despite slight drops in average accuracy. Even extremely compressed models like BERTTwy are shown to achieve a higher WGA than BERT Base- We hypothesize that this is due to CivilComments being dataset that BERT Base casily overfits As such, reduction in model size serves form of regularization for generalizing better across subgroups. Additionally, we note that a minimum model size is required for fitting the minority subgroups. Specifically, the WGA of distilled models with layers fewer
Task Complexity and Subgroup Robustness To understand the effects of task complexity on subgroup robustness, we construct 3 additional datasets by converting MultiNLI into a binary task. From Figure 2, model performance is shown to improve across the binary datasets for most mod- els. WGA improves the least when Y= [0, 2], i.e. when sentences contradict or neutral with one another. Additionally, although there is overall improvement in model performance, the trend in WGA remains relatively unchanged as seen in Fig- ure 1. A decreasing model size accompanied by a reduction WGA for most models. We hypoth-

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esize that subgroup robustness is less dependent on the task complexity as defined by number of subgroups that must be fitted.
accreditation CEX2021-001142-3 MICIN/ ABI/ 10.13039/501 1033. Additionally, we would like to thank Justina Li, Qiwei Peng, and Myles Bartlett for proof reading the paper.
Distribution of Subgroup Performance. We plot the accuracies distributed across subgroups in Figure 3. We limit our analysis to MultiNLI and CivilComments with KD for visual clarity. From Figure 3, we observe that model compression does not always maintain overall performance by sac- rificing the minority subgroups. In MultiNLI, decreasing model size reduces the accuracy on mi- nority subgroups (3 and 5) with the exception of TinyBERT g- Conversely, most compressed models improve in accuracy on the minority subgroups (2 and 3) in CivilComments. This shows that model compression does not necessarily cannibalize the performance on minority subgroups to maintain overall performance, but may improve performance across all subgroups instead.
References
Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, and Novi Quadrianto. 2022. Okapi: Generalising better by making statistical matches match. In Advances in Neural Information Processing Systems, volume 35, pages 1345-1361. Curran Associates, Inc.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, 1lya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners.
5 Conclusion
Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada. Sebastian Felix Schwemer. and Anders Sø- gaard. 2022. Fairlex: A multilingual benchmark for evaluating faimess in legal text processing.
In this work, we presented an analysis of existing compression methods the subgroup robustness of LMs. We found that compression does not al- ways harm the performance on minority subgroups. Instead, on datasets that a model easily overfits on, compression can aid the learning of features that generalize better across subgroups. Lastly, com- pressed LMs with the same number of parameters can have varying performance due to differences in weight initialization after compression.
Jacob Devlin, Ming-War Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre- Pre-training of deep bidirectional transformers for language understand- ing.
Josh Gardner, Zoran Popović, and Ludwig Schmidt. 2023. Subgroup robustness grows on trees: An em- pirical baseline investigation.
Leonidas Gee, Andrea Zugarini, Leonardo Rigutini, and Paolo Torroni. 2022. Fast vocabulary transfer for language model compression. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 409- 416, Abu Dhabi, UAE Association for Computa- tional Linguisties.
Limitations
Our work is limited by its analysis English lan- guage datasets. The analysis can be extended to other multi-lingual datasets from the recent FairLex benchmark (Chalkidis et al., 2022). Additionally, we considered each compression method in isola- tion and not in combination with one another.
Song Han, Jeff Pool, John Tran, and William J. Dally. 2015. Learning both weights and connections for efficient neural networks.
Aeknowledgements
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in neural network.
This research was supported by a European Re- search Council (ERC) Starting Grant for the project "Bayesian Models and Algorithms for Fairnsss and Transparency". funded under the European Union's 's Horizon 2020 Framework Programme (grant agree- ment no. 851538). Novi Quadrianto also sup- ported by the Basque Government through the BERC 2022-2025 program and by the Ministry of Science and Innovation: BCAM Severo Ochoa
Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, and Andrea Frome. 2021. What do com- pressed deep neural networks forget?
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton. 2020. Characterising bias compressed models.
Pavel Izmailov, Polina Kirichenko, Nate Gruver, and Andrew Gordon Wilson. 2022. On feature learning in the presence of spurious correlations.

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Iulia Turc, Ming- Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Well-read students learn better: On the importance of pre-training compact models.
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A Further Details
further in-domain distillation on MultiNLI. Addi- tionally, model performance is shown benefit the least when knowledge from the embedding is included during distillation. This can be seen by the lower WGA of TinyBERT AHE compared to TinyBERT, A/l across most datasets.
A.1 Datasets
We tabulate the labels and attributes that define each subgroup in Table Additionally, we show the sample size of each subgroup in the training, validation, and test sets.
A.2 Results
We tabulate the main results of the paper in Table 3. The performance of each model is averaged across
5 seeds.
B Additional Experiments
B.1 Sparsity and Subgroup Robustness.
Besides structured pruning, we investigate the ef- fects of unstructured pruning using 4 similar levels of sparsity. Connections are pruned via PyTorch by sorting the weights of every layer using the L1-norm. We tabulate the results separately in Ta- ble 4 as Py'Torch does not currently support sparse neural networks. Hence, no reduction in model size is seen in practice. From Table 4, we observe similar trends to those in Figure 1. Specifically, sparsity increases, the WGA generally worsens in MultiNLI and SCOTUS, but improves in Civil- Comments across most models. At sparsity of 80%, WGA drops significantly for MultiNLI and SCOTUS, but not for CivilComments.
B.2 Ablation of TinyBERT
To better understand the particular subgroup ro- bustness of Tiny we conduct an ablation on its general distillation procedure. Specifically, we ablate the attention matrices, hidden states, and em- beddings as sources of knowledge when distilling on the Wikipedia dataser The same hyperparame- ters as Jiao et al. (2020) are used except for a batch size of 256 and gradient accumulation of 2 due memory constraints.
From Table 5, we find that we are unable to achieve a similar WGA on MultiNLI and its bi- nary variants as shown by the performance gap be- tween TinyBERT, and TinyBERT, AHE- On SCO- TUS, the WGA of TinyBERT AHE is found to also be much higher than TinyBERT, We hy- pothesize that the pre-trained weights that were uploaded to HuggingFace may have included
htttss:/huuggngfface

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Dataset Subgroup
Label
Attribute
Training
Validation
Test
0
(Contradicts)
0 (No negation)
57498
22814
34597
1
0 (Contradicts)
(Has negation)
11158
4634
6655
MultiNLI
2
(Entails)
0 (No negation)
67376
26949
40496
3
(Entails)
(Has negation)
1521
613
886
4
(Neutral)
0 (No negation)
66630
26655
39930
5
(Neutral)
(Has negation)
1992
797
1148
CivilComments
0
0 (Neutral)
0 (No sensitive)
148186
25159
72373
1
0 (Neutral)
(Has sensitive)
90337
14966
46185
2
(Toxic)
0 (No sensitive)
12731
2111
6063
3
(Toxic)
(Has sensitive)
17784
2944
9161
(Criminal Procedure)
(Liberal)
869
111
114
O (Criminal Procedure)
(Conservative)
701
82
101
2
(Civil Rights)
0 (Liberal)
536
67
72
3
(Civil Rights)
(Conservative)
683
87
90
4
(First Amendment)
0 (Liberal)
267
29
37
5
(First Amendment)
(Conservative)
334
44
39
6
3 (Due Process)
0 (Liberal)
130
18
23
7
3 (Due Process)
(Conservative)
162
18
24
8
(Privacy)
(Liberal)
66
9
(Privacy)
(Conservative)
29
SCOTUS
10
5 (Attorneys)
(Liberal)
34
11
5 (Attorneys)
(Conservative)
32
12
(Unions)
0 (Liberal)
149
10
20
13
6 (Unions)
(Conservative)
174
17
28
14
7 (Economic Activity)
0 (Liberal)
644
87
70
15
(Economic Activity)
(Conservative)
925
105
112
16
(Judicial Power)
(Liberal)
689
75
17
(Judicial Power)
(Conservative)
384
41
18
9 (Federalism)
(Liberal)
144
19
9 (Federalism)
(Conservative)
189
31
22
20
10 (Interstate Relations)
0 (Liberal)
67
16
4
21
10 (Interstate Relations)
(Conservative)
209
24
29
Table 2: Defined subgroups in MultiNLI, CivilComments, and SCOTUS.

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MultiNLI
CivilCoommets
SCOTUS
Model
Average
Worst
Average
Worst
Average
Worst
BERT Base
82.58
64.06
92.96
52.22
82.62
50.36
BERTMediam Medium
BERT
79.02
55.46
92.70
55.02
80.50
38.18
BERT Small
75.86
47.40
92.70
55.44
79.06
40.76
BERT Mini
72.62
40.50
92.56
56.46
75.12
0.00
BERT / Vrey
64.90
19.26
92.04
54.26
63.02
0.00
DistilBERT
80.14
58.40
92.90
50.42
81.02
46.40
TinyBERT 85.26
72.74
92.80
52.72
77.58
29.78
TinyBERT
80.60
57.70
92.60
56.20
62.08
0.00
PERT
82.08
61.24
92.80
56.64
81.84
44.00
BERTPR40 81.50
60.92
92.84
54.62
81.04
50.00
BERTPR60
80.72
61.24
92.86
53.08
80.28
36.64
BERTPR50 78.78
52.64
92.76
52.94
79.94
33.30
BERT 76.74
58.48
92.48
36.96
72.44
3.34
BERT SQ
77.98
57.64
92.40
35.16
75.30
16.36
BERTQAT 79.44
57.26
92.50
60.24
80.20
43.20
BERT 82.30
64.40
92.98
50.28
82.54
42.48
BERT V775
82.26
62.24
93.00
51.96
81.62
36.12
BERT
82.00
62.80
93.00
50.86
82.10
50.98
BERT V725
81.26
63.92
92.90
52.14
81.42
40.44
(a) MultiNLI, CivilComments,, and SCOTUS.
= 1]
Y= [0, 2]
Y 1=11, 2]
Model
Average
Worst
Average
Worst
Average
Worst
BERT Base
92.54
85.24
86.74
67.80
87.70
84.52
BERT Mediam
89.28
78.70
83.40
57.84
85.80
81.62
BERT Small
87.26
77.32
80.90
51.14
84.16
82.58
BERT Mini
85.10
70.54
78.04
42.50
82.76
80.38
BERT iny
79.16
64.22
73.28
14.08
77.58
68.36
DistilBERT
90.52
84.00
84.48
62.02
86.02
83.88
TinyBERT, 94.24
89.06
89.44
76.50
89.40
87.62
TinyBERT
91.10
83.04
85.80
60.84
86.34
83.60
BERT P20
92.48
85.72
86.50
69.12
87,30
83.34
BERTPR40
92.14
84.64
85.88
66.64
86.86
82.76
BERTPR60
91.50
82.54
84.86
62.00
86.40
82.14
BERT P7880
89.78
79.48
82.76
50.24
85.02
81.98
BERT DQ
90.76
81.08
85.04
62.18
85.58
79.02
BERT
87.26
71.84
83.12
58.30
83.00
73.42
BERTQAY
90.12
80.14
84.72
67.74
85,90
82.78
BERT V7100
92.32
85.86
86.22
66.14
87.50
84.92
BERT VT75
92.28
84.20
86.16
65.88
87.56
85.16
BERT V750
92.16
85.30
86.12
60.50
87.30
84.40
BERTyT25
91.46
84.82
85.24
62.54
86.78
82.34
(b) MuliiLI with different binary labels.
Table 3: Model performance averaged across 5 seeds. WGA decreases model size is reduced in MultiNLI and SCOTUS, but increases instead in CivilComments, This trend is also seen the binary variants of MultiNLI despite reduction task complexity.

---

MultiNLI
CivilComments
SCOTUS
Sparsity
Average
Worst
Average
Worst
Average
Worst
0%
82.58
64.06
92.96
52.22
82.62
50.36
20%
81.92
66.20
92.66
57.12
82.42
44.62
40%
81.84
67.30
92.74
58.26
82.34
50.82
60%
81.44
63.12
92.84
55.36
81.52
50.00
80%
74.32
40.34
92.32
53.40
74.40
0.00
(a) MultiNLI, CivilComments, and SCOTUS.
Y [0,
= [0, 2]
Y [1, 2]
Sparsity
Average
Worst
Average
Worst
Average
Worst
0%
92.54
85.24
86.74
67.80
87.70
84.52
20%
92.54
85.32
86.48
69.48
87.02
82.34
40%
92.38
85.28
86.38
69.16
87.16
84.06
60%
91.90
84.52
85.76
66.70
86.76
84.50
80%
86.00
70.38
78.52
38.72
81.78
76.28
(b) MultiNLI with different binary labels.
Table 4: Average and worst-group accuracies for unstructured pruning. BERT Bose is shown with sparsity of 0%.
MultiNLI and SCOTUS generally see a worsening WGA when sparsity increases contrary to the improvements CivilComments.
MultiNLI
CivilComments
SCOTUS
Model
Average
Worst
Average
Worst
Average
Worst
TinyBERT
85.26
72.74
92.80
52.72
77.58
29.78
TinyBERT ARE
77.78
53.96
92.56
52.88
77.28
38.70
TinyBERT AH
77.76
54.50
92.54
53.76
76.70
38.94
TinyBERT AUE
77.14
52.24
92.56
53.22
76.82
37.48
TinyBERT HE
75.68
51.66
92.54
51.70
77.76
39.26
TinyBERT
77.12
51.62
92.58
53.42
77.14
34.98
TinyBERT H
76.08
53.12
92.52
50.82
78.06
42.04
TinyBERT
65.90
32.22
92.00
44.16
75.58
33.30
MultiLLL CivilComments, and SCOTUS.
Y= [0, 1]
Y= [0, 2]
Y [1, 2]
Model
Average
Worst
Average
Worst
Average
Worst
TinyBERT;
94.24
89.06
89.44
76.50
89.40
87.62
TinyBERT AHE
88.82
78.90
81.88
51.26
84.58
82.82
TinyBERT AH
88.82
78.50
81.72
53.04
84.62
82.04
TinyBERT AE
88.12
78.32
80.86
48.78
83.90
81.58
TinyBERT HE
87.48
75.88
80.60
49.74
83.36
80.56
TinyBERT,
87.98
78.50
80.86
49.76
83.76
81.70
TinyBERT H
87.76
76.26
80.94
53.80
83.58
79.80
TinyBERT 78.68
61.10
72.84
38.74
72.22
65.80
(b) MultiNLI with different binary labels.
Table 5: Ablation of TinyBERT, The subscripts A, H, and E represent the attention matrices, hidden states, and embeddings that are transferred as knowledge respectively during distillation. A noticeable performance gap is seen between TinyBERT, and TinyBERT, ARE MultiNLI and SCOTUS.


Reviews:
[{"review_id": "J62bRBVOW8", "reviewer": "Reviewer_gpj9", "summary": "", "questions": "My questions follow my two reasons to reject above:\n\n1. Do you think CivilComments as a dataset is an outlier in that its subgroup performance actually improved?\n2. Why do you think certain compression methods work better than others? Is parameter a potential confound here since different methods yield models of different sizes?", "limitations": "", "rating": 3, "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": "Hi, thank you for the feedback regarding the paper. We address your concerns and questions below.\n\nReasons To Reject:\n\n**I would have appreciated more datasets, esp given that lots of compressed models actual perform better than the vanilla one on CivilComments. It would have helped to have more results to know whether it's an outlier or genuinely interesting trends.**\n\nWe addressed the following issue in the official comment.\n\n**I find the overall conclusion a bit lackluster, esp given the absence of any substantive analysis on why certain methods perform better than others.**\n\nWe addressed the question of why certain methods perform better than others further below.\n\nQuestions For The Authors:\n\n**Do you think CivilComments as a dataset is an outlier in that its subgroup performance actually improved?**\n\nWe address the following issue in the “Additional Experiments” section of the official comment.\n\n**Why do you think certain compression methods work better than others? Is parameter a potential confound here since different methods yield models of different sizes?**\n\nWe noted that methods such as post-training quantization (BERT_DQ and BERT_SQ) which do not possess an additional fine-tuning step after compression (e.g. pruning) or a compression-aware training (BERT_QAT) are naturally less subgroup robust. These methods do not allow the model to recover performance after compression or to prepare for compression by learning compression-robust weights.\n\nDirectly comparing different compression methods is difficult as different methods compress different parts of the model. For example, the compression of vocabulary transfer is upper bounded by the size of the embedding matrix. However, plotting different compression methods together provides us with a useful general overview of the effects of compression on subgroup robustness.", "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. Many different flavors of model compression methods are tested. The results are definitely helpful for those shopping for more subgroup-robust techniques.\n2. Very clearly written and easy to understand.", "reasons_to_reject": "1. I would have appreciated more datasets, esp given that lots of compressed models actual perform better than the vanilla one on CivilComments. It would have helped to have more results to know whether it's an outlier or genuinely interesting trends.\n2. I find the overall conclusion a bit lackluster, esp given the absence of any substantive analysis on why certain methods perform better than others.", "questions_to_authors": "My questions follow my two reasons to reject above:\n\n1. Do you think CivilComments as a dataset is an outlier in that its subgroup performance actually improved?\n2. Why do you think certain compression methods work better than others? Is parameter a potential confound here since different methods yield models of different sizes?", "paper_topic_and_main_contributions": "This paper is an empirical study of how different methods of model compression affect performance on minority subgroups. 18 methods are tested on two datasets, and the results show that worst-group performance depends on both the method and model size.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "I find the 'size' of the markers in Figure 1 unintuitive, esp when you have many different shapes that make it hard to compare."}, "extra_text": {}}, {"review_id": "oylwfGKutX", "reviewer": "Reviewer_RMg6", "summary": "", "questions": "1. Why did you choose MultiNLI and CivilComments? The motivation is not clearly stated. \n2. The subgroup definition in each dataset is unclear", "limitations": "", "rating": 3, "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": "Hi, thank you for the feedback regarding the paper. We address your concerns and questions below.\n\nReasons To Reject:\n\n**Some key descriptions are missing, e.g., concept and abbreviation definition, the selection of datasets, and how to divide each dataset into subgroup partitions. Therefore, this makes it hard to understand the paper**\n\nThank you for the observation. Given the limited space of the short paper, we did not include a definition of each compression method. However, we will incorporate these definitions in the revised manuscript version. We detailed the choice of datasets  below and the subgroup partitions in the official comment.\n\n**I'm struggling to find the main conclusion in each experiment, especially for Figure 1 and 2**\n\nRegarding Figure 1, we show how different model sizes and compression methods affect the subgroup robustness of a model. We observe that compression does not always worsen the subgroup robustness particularly on datasets that the baseline model overfits on (i.e. CivilComments). In such cases, a reduction in model size can help achieve a better fit on the minority subgroups.\n\nRegarding Figure 2, we break down the performance into subgroups for MultiNLI and CivilComments with knowledge distillation. We show that model compression does not always maintain overall performance by sacrificing the minority subgroups (Hooker et al., 2021, 2020; Stoychev and Gunes, 2022).\n\nQuestions For The Authors:\n\n**Why did you choose MultiNLI and CivilComments? The motivation is not clearly stated.**\n\nAs mentioned in the Limitations section, L273-274,  MultiNLI and CivilComments are benchmark datasets used by most subgroup robustness research (Sagawa et al., 2020; Koh et al., 2021; Izmailov et al., 2022). Thank you for the observation, we will make it more clear and also add a statement within the paper to better clarify this aspect, which is actually relevant to frame the contribution of the paper.\n\n**The subgroup definition in each dataset is unclear**\n\nSee the official comment.", "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. This work first investigates the correlation between the compression method and subgroup robustness\n2. The experiments include a wide range of compression methods, which might be useful for future studies", "reasons_to_reject": "1. Some key descriptions are missing, e.g., concept and abbreviation definition, the selection of datasets, and how to divide each dataset into subgroup partitions. Therefore, this makes it hard to understand the paper\n2.  I'm struggling to find the main conclusion in each experiment, especially for Figure 1 and 2", "questions_to_authors": "1. Why did you choose MultiNLI and CivilComments? The motivation is not clearly stated. \n2. The subgroup definition in each dataset is unclear", "paper_topic_and_main_contributions": "This work conducts an analysis of various compression methods on the subgroup robustness of LMs.\nThey test 18 different compression methods on MultiNLI and CivilComments. Empirical studies show that \nthe worst-group robustness relies on both model size and compression method.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. Giving a brief definition of a concept after its first appearance is helpful for understanding, e.g., subgroup, KD, size of a model, and attribute.\n2. In Figure 1, it is hard to distinguish model sizes"}}, {"review_id": "wdQ8ZxnzhW", "reviewer": "Reviewer_GktP", "summary": "", "questions": "", "limitations": "", "rating": 2, "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": "Hi, thank you for the feedback regarding the paper. We address your concerns and questions below.\n\nReasons To Reject:\n\n**No novel techniques or salutations are proposed. This is a survey paper.**\n\nThe objective of our paper is to understand how existing methods affect the subgroup robustness of compressed models. We do not propose a novel compression method, which is out of the scope of this paper. By understanding which methods retain the highest subgroup robustness, we will be better able to develop novel solutions from those methods.\n\n**The paper focuses on BERT-style models. It will be more interesting to explore how recent compressions and quantization affects LLM models like LLAMA.**\n\nThe field of subgroup robustness research is focused on classification tasks (Sagawa et al., 2020; Koh et al., 2021; Izmailov et al., 2022). As such, generative models like LLAMA are not best suited for this kind of problems, and we consider them out of the scope of our work. Nonetheless, investigating generative tasks is an interesting direction that we would like to pursue in a future work.\n\n**The paper uses only two datasets. There are many other standard classification datasets that could have been included.**\n\nWe address this concern in  the official comment.\n\n**The subgroup partitioning is not clearly defined. The details are delegated to the appendix but are still not clear.**\n\nWe detail this aspect in the official comment.\n\n**Lacks deeper analysis on why TinyBERT is an outlier with regards to subgroup robustness. What are the specific things that make TinyBERT better, some ablation study is warranted.**\n\nWe agree that TinyBERT requires further ablation study on its knowledge distillation process in order to understand why it displays a high subgroup robustness particularly on MultiNLI. At the time, we hypothesized that the more comprehensive knowledge distillation of TinyBERT involving not just the predictions, but also the hidden states, attention matrices, and embeddings may be responsible for its higher subgroup robustness.", "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 the problem is relevant to the NLP community.\n* Surveys a large number of techniques and models for compression, and their effect on subgroup robustness.", "reasons_to_reject": "* No novel techniques or salutations are proposed. This is a survey paper.\n* The paper focuses on BERT-style models. It will be more interesting to explore how recent compressions and quantization affects LLM models like LLAMA. \n* The paper uses only two datasets. There are many other standard classification datasets that could have been included.\n* The subgroup partitioning is not clearly defined. The details are delegated to the appendix but are still not clear. \n* Lacks deeper analysis on why TinyBERT is an outlier with regards to subgroup robustness. What are the specific things that make TinyBERT better, some ablation study is warranted.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper explores how different compressions techniques for Transformer-encoder models like BERT and ROBERTA affect the classification robustness for minority classes and subgroups. The authors explore 18 different techniques and models available from HuggingFace. An interesting finding is that compression improves subgroup robustness and smaller models are not always worse.", "excitement": 2, "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": {}}]

Author rebuttal:

Meta-review: This paper explores how the model compression impacts the models' robustness on minority subgroups. The authors present experiments with 18 models compressed by different methods or settings, on 3 datasets (the last one is added during the discussion period). 

As the reviewers mentioned, the paper includes a large list of models including knowledge distillation, post-training quantization, quantization-aware training, structured pruning, and vocabulary transfer. This is comprehensive and helpful for future studies. In terms of datasets, the authors provided further context for the dataset selection during the discussion period. Furthermore, they presented another set of experiments on a new dataset addressing reviewers' feedback.

However, also should be noted that reviewers pointed out a lack of analysis on why certain compression methods/models work better than others such as TinyBert. Although the authors attempted to address this during the discussion period, the paper would still benefit from a deeper analysis.
Decision: Accept-Main