Original: Understanding Out-of-distribution: A Perspective of Data Dynamics
Revised: Understanding Out-of-distribution:A Perspective of Data Dynamics

Original: Anonymous Author(s)

Original: AfﬁliationAddress email

Original: Abstract
Revised: Abstract

Original: Despite machine learning models’ success in Natural Language Processing (NLP)tasks, predictions from these models frequently fail on out-of-distribution (OOD)samples.
Revised: Despite machine learning models’ success in Natural Language Processing (NLP) tasks, predictions from these models frequently fail on out-of-distribution (OOD) samples.

Original: Prior works have focused on developing state-of-the-art methods fordetecting OOD.
Revised: Prior works have focused on developing state-of-the-art methods for detecting OOD.

Original: The fundamental question of how OOD samples differ from indistribution samples remains unanswered.
Revised: The fundamental question of how OOD samples differ from indistribution samples remains unanswered.

Original: This paper explores how data dynamicsin training models can be used to understand the fundamental differences between
Revised: This paper explores how data dynamics in training models can be used to understand the fundamental differences between

Original: OOD and in-distribution samples in extensive detail.
Revised: OOD and in-distribution samples in extensive detail.

Original: We found that syntacticcharacteristics of the data samples that the model consistently predicts incorrectlyin both OOD and in-distribution cases directly contradict each other.
Revised: We found that syntactic characteristics of the data samples that the model consistently predicts incorrectly in both OOD and in-distribution cases directly contradict each other.

Original: In addition,we observed preliminary evidence supporting the hypothesis that models are morelikely to latch on trivial syntactic heuristics (e g., overlap of words between twosentences) when making predictions on OOD samples.
Revised: In addition, we observed preliminary evidence supporting the hypothesis that models are more likely to latch on trivial syntactic heuristics (e.g., overlap of words between two sentences) when making predictions on OOD samples.

Original: We hope our preliminarystudy accelerates the data-centric analysis on various machine learning phenomena.
Revised: We hope our preliminary study accelerates the data-centric analysis on various machine learning phenomena.

Original:  Introduction
Revised:  Introduction

Original: Detecting out-of-distribution (OOD) has become one of the key bottlenecks in building reliable openworld systems [2], leading to new SOTA approaches meant to mitigate this problem [4, 12, 14, 16, 19].
Revised: Detecting out-of-distribution (OOD) has become one of the key bottlenecks in building reliable open-world systems [3], leading to new SOTA approaches meant to mitigate this problem [5, 16, 18, 29, 20, 23].

Original: Previous works have explored the discrepancy between OOD and in-distribution test performancefrom the model and algorithm perspective.
Revised: Previous works have explored the discrepancy between OOD and in-distribution test performance from the model and algorithm perspective.

Original: Le Lan & Dinh (2020) [13] observed the limitation ofdensity estimation for anomaly detection.
Revised: Le Lan & Dinh (2020) [17] observed the limitation of density estimation for anomaly detection.

Original: Zhang et al.
Revised: Zhang et al.

Original: [28] suggested that the cause of OODfailure is the combination of the model architecture and maximum likelihood objective.
Revised: [37] suggested that the cause of OOD failure is the combination of the model architecture and maximum likelihood objective.

Original: Choi et al.(2018) [5], Just & Ghosal (2019) [11], Fetaya et al.
Revised: Choi et al. (2018) [6], Just & Ghosal (2019) [14], Fetaya et al.

Original: [8], Kirichenko et al.
Revised: (2020) [9], Kirichenko et al.

Original: (2020) [1], Zhang etal.
Revised: (2020) [15], Zhang et al.

Original: (2020) [27], and Wang et al.
Revised: (2020) [36] and Wang et al.

Original: [25] concluded that signiﬁcant mismatch between the realand estimated distribution gives rise to the performance discrepancy.
Revised: [34] concluded that significant mismatch between the real and estimated distribution gives rise to the performance discrepancy.

Original: Nevertheless, what fundamentaldifferences in OOD and in-distribution  samples  cause them to be separated in their data distributions?
Revised: Nevertheless, what are the fundamental differences between in-distribution and OOD data samples which cause models to be especially brittle to the latter?

Original: In this work, we propose a new perspective on this discrepancy and examine the differences between
Revised: Our approach to studying this difference is inspired by emerging studies on machine learning models adopting shallow heuristics (i.e., irrelevant statistical patterns found in the majority of training examples), instead of learning the underlying generalizations that they are intended to capture [22, 33, 1, 2]. For example, Beery et al. [2] demonstrated a network that accurately recognizes cows in a typical context (e.g., pasture) consistently misclassifies cows in a non-typical context (e.g., beach).

Original: OOD and in-distribution data samples by considering the training dynamics of the model .
Revised: Similar heuristics also arise in visual question answering systems [1] and researchers proposed graph generative modeling schemes [13] (inspired by graph convolutional networks [30]) to handle the problem implicitly.

Original: We studythis problem within the Natural Language Inference (NLI): the task of determining whether a premisesentence entails (i.e., implies the truth of) a hypothesis sentence [6, 7, 3].
Revised: In this paper, we study this problem within the Natural Language Inference (NLI): the task of determining whether a premise sentence entails (i.e., implies the truth of) a hypothesis sentence [7, 8, 4].

Original: We perform two types of data-dynamic analyses: We ﬁrst examine the difference between OODand in-distribution samples distributions in the data cartography space [23] at each training epoch.
Revised: McCoy et al. [22] exhaustively characterized shallow heuristics that commonly appears in benchmark NLI datasets, which we will refer to as syntactic heuristics . To this end, we study the difference between in-distribution and OOD samples which gives rise to models brittleness through two perspectives: (i) training dynamics of the model, and (ii) syntactic heuristics of the data samples. Specifically, we perform two types of data-dynamic analyses: We first examine the difference between OOD and in-distribution samples distributions in the data cartography space [28] at each training epoch.

Original: Cartography space allows us to distinguish which samples are harder to learn (i.e., the model oftenmisclassiﬁes them) based on training dynamics measures.
Revised: Cartography space allows us to distinguish which samples are harder to learn (i.e., the model often misclassifies them) based on training dynamics measures.

Original: Second, we add syntactic heuristic [18] asanother component in this analysis, which enables us to identify what data characteristics tend to beharder to learn.
Revised: Second, we mark each data sample by their syntactic heuristic, which enables us to identify what shallow characteristics tend to be harder to learn.

Original: For instance, we hypothesize whether more word overlap between a premise and ahypothesis text inﬂuences model’s ability to infer their label correctly.
Revised: For instance, we hypothesize whether more word overlap between a premise and a hypothesis text influences model’s ability to infer their label correctly.

Original: Our analyses suggested that the syntactic heuristic that the model deems hard-to-learn during trainingdirectly contradicts the characteristic of OOD samples  are hard-to-learn during inference.
Revised: Our analyses suggested that the syntactic heuristic that the model deems hard-to-learn during training directly contradicts the characteristic of OOD samples that are hard-to-learn during inference.

Original: We alsofound preliminary evidence suggesting the model’s tendency to make inferences based on trivialsyntactic heuristic is higher in the OOD case.
Revised: We also found preliminary evidence suggesting the model’s tendency to make inferences based on trivial syntactic heuristic is higher in the OOD case.

Original: We hope that this study will drive more effort to betterunderstand the difference between in-distribution and OOD samples and, consequently, develop moreinformed and data-centric OOD detection and generalization methods.
Revised: We hope that this study will drive more effort to better understand the difference between in-distribution and OOD samples and, consequently, develop more informed and data-centric OOD detection and generalization methods.

Original:  Our Hypotheses
Revised:  Our Hypotheses

Original: This section presents our two initial hypotheses to characterize OOD data samples.
Revised: This section presents our two initial hypotheses to characterize OOD data samples.

Original: Motivated byprevious works [9, 10], we perform a comparative analysis between OOD and in-distribution samplesover dynamic information of model training (e.g., conﬁdence, prediction variance across epoch) astraining progresses.
Revised: Motivated by previous works [11, 12], we perform a comparative analysis between OOD and in-distribution samples over dynamic information of model training (e.g., confidence, prediction variance across epoch) per set of train-test epoch.

Original: Moreover, to interpret the quantitative difference of these measures, we mark datasamples based on their syntactic heuristics , such as word overlap between two texts.
Revised: Moreover, to interpret the quantitative difference of these measures, we mark data samples based on their syntactic heuristics .

Original: We hypothesizethat the difference between OOD and in-distribution samples will be more clearly visible in thecombination of multiple training dynamics (§2.1) and sample heuristics (§2.2).
Revised: We hypothesize that the difference between OOD and in-distribution samples can be characterized in the combination of multiple training dynamics (§2.1) and sample heuristics (§2.2).

Original: 2. H1: Are OOD and in-distribution samples different with their training dynamics?
Revised: H1: Are OOD and in-distribution samples different with their training dynamics?

Original: Data cartography [23] is a tool to group data samples into three regions: easy-to-learn, hard-tolearn, and ambiguous (see Figure 1), enabled by following two training dynamics as axes:(i)
Revised: Data cartography [28] is a tool to group data samples into three regions: easy-to-learn, hard-to-learn, and ambiguous (see Figure 1), enabled by following two training dynamics as axes:

Original: Conﬁdence is deﬁned as the mean model probability of the true label ( y ∗ i ) across epochs (equation1).
Revised: Confidence : the mean model probability of the true label ( y ∗ i ) across epochs (equation 1).

Original: The term p ( e ) θ ( y ∗ i | x i ) denotes the model’s probability with parameters θ at the end of e th epoch.(ii) Variability measures the spread of p ( e ) 
Revised: The term p ( e ) θ ( y ∗ i | x i ) denotes the model’s probability with parameters θ at the end of e th epoch. Variability : the spread of p ( e ) θ

Original: θ ( y ∗ i | x i ) across epochs, and is deﬁned by equation 2.
Revised: ( y ∗ i | x i ) across epochs, and is defined by equation 2.

Original: [Equation]
Revised: [Equation]

Original: Intuitively, higher variability implies a high range of probability outputted by the model for the samesample.
Revised: Intuitively, higher variability implies a high range of probability outputted by the model for the same sample.

Original: As seen in Figure 1, the rightmost region (i.e., high variability) is the ambiguous region.
Revised: As seen in Figure 1, the rightmost region (i.e., high variability) is the ambiguous region.

Original: Theeasy-to-learn region is characterized by high conﬁdence and low variability (i.e., correct predictionwith high assigned probability across epochs), and hard-to-learn region samples have low conﬁdenceand low variability (i.e., incorrect prediction across epochs). 
Revised: The easy-to-learn region is characterized by high confidence and low variability (i.e., correct prediction with high assigned probability across epochs), and hard-to-learn region samples have low confidence and low variability (i.e., incorrect prediction across epochs). At each epoch E , we record the following four measurements for all train samples i ∈ N train and test samples j ∈ N test ( N train and N test denote the size of train and test sets respectively). As θ is constant for all samples at each epoch, for conciseness sake, we abbreviate p ( e ) θ ( y ∗ i | x i ) as p ( e ) i .

Revised: [Equation]

Revised: [Equation]

Original: As models’ performance in inferring in-distribution samples far exceeds that of the OOD case, wehypothesize that most OOD samples will stay in hard-to-learn regions.
Revised: N [Equation] In each epoch E , we then plot all train samples based on their respective ( ˆ µ Ei , ˆ σ iE ) resulting in train cartography map C Etrain (e.g., figure 1a). Similarly, we also plot all test samples based on their ( ˆ µ Ej , ˆ σ jE ) resulting in test cartography map C Etest .

Revised: (e.g., figure 1c)

Revised: As models’ performance in inferring in-distribution samples far exceeds that of the OOD case, we hypothesize that most OOD samples will stay in the hard-to-learn regions in C Etest for all E .

Original: In contrast, most in-distributionsamples will eventually move to the easy-to-learn region, results in a noticeable difference betweenthe two distributions in the cartography space.
Revised: In contrast, most in-distribution samples will eventually move to the easy-to-learn region in C Etest for larger E , which results in a noticeable difference between the two distributions in the cartography space.

Original: We hope to interpret this difference using sytacticheuristic (§2.2).
Revised: We hope to interpret this difference using syntactic heuristic (§2.2).

Original:  H2: Do models tend to adopt syntactic attributes of OOD samples more readily?
Revised: 2. H2: Do models tend to adopt syntactic attributes of OOD samples more readily?

Original: Past works have shown that machine learning models often adopt ‘shortcut’ data characteristicsinstead of the generalization that humans would learn and use to perform the same task.
Revised: Past works have shown that machine learning models often adopt ‘shortcut’ data characteristics instead of the generalization that humans would learn and use to perform the same task [22, 33, 1, 2].

Original: For example,a model trained on NLI might assign a label of contradiction to any input containing the word “not”[20, 22], as this heuristic often applies in standard NLI training sets.
Revised: More specifically, a model trained on NLI might assign a label of contradiction to any input containing the word “not” [24, 26], as this heuristic often applies in standard NLI training sets.

Original: As OOD samples come froma different distribution, they will have less common generalizable characteristics with the trainingset than the in-distribution samples.
Revised: As OOD samples come from a different distribution, they will have less common generalizable characteristics with the training set than the in-distribution samples.

Original: Thus, we hypothesize that models will rely even more upon theshortcut characteristics (i.e., heuristics) when making inferences on OOD samples.
Revised: Thus, we hypothesize that models will rely even more upon the shortcut characteristics (i.e., heuristics) when making inferences on OOD samples.

Original: We focus on lexical overlap heuristic [18], as it is one of the simplest and most common across NLIdatasets.
Revised: We focus on lexical overlap heuristic [22], as it is one of the simplest and most common across NLI datasets.

Original: This heuristic assumes that a premise entails all hypotheses constructed from words in thepremise.
Revised: This heuristic assumes that a premise entails all hypotheses constructed from words in the premise.

Original: Below are examples:
Revised: Below are examples:

Original: [Table]
Revised: [Table]

Revised: Hypothesis

Original:  The judge was paid by the actor.
Revised: Label Type The judge was paid by the actor.

Original: The actor paid the judge.
Revised: The actor paid the judge.

Original: Entailed Support
Revised: Entailed Support

Original: The actor was paid by the judge. The actor paid the judge. 
Revised: The actor was paid by the judge. The actor paid the judge. Not entailed

Original: [Table]
Revised: Contradict

Original:  labels entailed and not entailed respectively), or (iii) having no heuristic.
Revised: A sample can either: (i) supporting, (ii) contradicting (i.e., possessing the property of heuristic and labels entailed or not entailed respectively), or (iii) having no heuristic.

Original: We mark each sample’sheuristics, as illustrated by the different circle colors of Figure 1.
Revised: We mark each sample’s heuristics, as illustrated by the different circle colors of Figure 1.

Original: To quantify this, we use the following measure: m 2 = | s 1 (cid:84) s 2 | | s 2 | . Essentially, m 2 measures how muchpercentage of words in the  premise is found in the hypothesis. This aligns with the lexical overlapdeﬁnition: all words in the hypothesis (i.e., s 2 ) is contained in the premise (i.e. s1 ).
Revised: To quantify the degree of which each sample adopts lexical heuristic, we calculate the percentage of words in the hypothesis (s2) which overlap with premise (s1), which aligns with the lexical heuristic definition. More formally, we measure m 2 = | s

Original: [Figure]
Revised: [Equation]

Original:  To measure themodel’s tendencyto adapttheheuristic, wecalculate the correlation betweenm 2 and conﬁdence ˆ µ i andobserve the trendacross epochs.
Revised: s 2 | | s 2 | . To measure the model’s tendency to adapt the heuristic, we calculate three sets of correlations: (i) ρ ( m 2 , µ Ei ) (ii) ρ ( m 2 , µ Ej ( in − dist ) ) (iii) ρ ( m 2 , µ Ej ( OOD ) ) , and observe the trends across epochs.

Original: We calculate thiscorrelation forall samples andalso for samplesfrom each classindependently.
Revised: We calculate these correlations for all samples and also for samples from each class independently.

Original: We then observehow these trendsvary as trainingprogresses.

Original: Intuitively, a highheuristic adoption is indicatedby high absolutecorrelation valuesin the individualclass samples(e.g., the modellearns that highwords overlap means the two sentences are entailed, vice versa).
Revised: Intuitively, a high heuristic adoption is indicated by high absolute correlation values in the individual class samples (e.g., the model learns that high words overlap means the label is entailment , vice versa).

Original:  Experiments
Revised:  Experiments

Original: [Table]

Original: MNLI

Original: [Table]
Revised: [Table]

Original: train

Original: [Table]

Original: Setup:
Revised: Setup:

Original: In all experiments, we trained Roberta[17] with batch size 20 and learning rate 1 . 1 e − 5 ,and initialized with the same random seed.
Revised: In all experiments, we trained Roberta [21] with batch size 20 and learning rate 1 . 1 e − 5 , and initialized with the same random seed.

Original: Allexperiments were carried out using PyTorch [21]with one NVidia Tesla K20X GPU.
Revised: All experiments were carried out using PyTorch [25] with one NVidia Tesla K20X GPU.

Original: Experimentswere carried out using three datasets (MNLI[26], RTE [24], WNLI [15]) which combinations are shown in table 1.
Revised: Experiments were carried out using three datasets (MNLI [35], RTE [31], WNLI [19]) which combinations are shown in table 1.

Original: Our extensive experiments results can be found in Appendix A.3,
Revised: In our study, we define OOD samples as those with different textual genre and sentence structure from the training. For instance, WNLI were derived by taking sentences from fiction books. Each [Figure]

Original:  Appendix A.1, and Appendix A.2.
Revised: hypothesis-premise pair is a sentence and it’s copy with the ambiguous pronouns replaced by each possible referent. While RTE and MNLI’s hypothesis-premise pairs are different sentences. RTE are texts from Wikipedia and MNLI were crowdsourced from speech, fiction, and government report. Our extensive experiments results can be found in Appendix A.3, Appendix A.1, and Appendix A.2.

Revised: For conciseness sake, we only present results with MNLI as train and in-distribution data, and WNLI as OOD data.

Original: 3.1 OOD vs in-distribution on training dynamics information
Revised: 3.1 OOD vs in-distribution on training dynamics and syntactic heuristic

Original: Result for this section are the cartography maps  in Figure 1.
Revised: Result for this section are the cartography maps C 2 train , C 8 train , C 2 test , C 8 test in Figure 1.

Original: For visualization clarity, the pointsshowed are a sampled fraction from the original set.
Revised: For visualization clarity, the points showed are a sampled fraction from the original set.

Original: We observe the following phenomena: Acontradicting pattern between the trajectories of training and OOD samples.
Revised: We observe the following: • Observation: A contradicting pattern between the trajectories of training and OOD samples.

Original: By comparing ﬁgures(a) and (b) (beginning and end of the training, respectively), we observed samples that contradictthe heuristic (blue circles) either stays in the hard-to-learn region or move towards the ambiguousregion.
Revised: By comparing figures (a) and (b) ( C 2 train and C 8 train ) we observed samples that contradict the heuristic (blue circles) either stays in the hard-to-learn region or move towards the ambiguous region.

Original: On contrary, the evaluation maps (ﬁgures (c) and (d)) shows that OOD samples that supportthe heuristic (green ’x’s) stay in the hard-to-learn region.
Revised: On contrary, the figures (c) and (d) ( C 2 test and C 8 test ) shows that OOD samples that support the heuristic (green ’x’s) stay in the hard-to-learn region.

Original: This implies that samples characteristics
Revised: Conjecture: Samples heuristics (i

Original: (i.e., lexical overlap heuristic) that the model deems hard/easy to learn during training completelyﬂips in the OOD case (especially during initial epochs).
Revised: e., lexical overlap) that the model deems hard/easy to learn during training completely flips in the OOD case . • Observation:

Original: At the end of epoch 8, we observe relatively more color mix at the ambiguous region for the OODcase (blue and green ’x’s at the ambiguous region of map (d)).
Revised: At the end of epoch 8, we observe way less color mix at the ambiguous region of C 8 test (blue and green ’x’s at the ambiguous region of map (d)).

Original: While in the training epoch 8, this mixis more apparent towards the easy-to learn region (blue and green circles).
Revised: While in C 8 test (map (b)), this mix is very apparent in the easy-to learn region (blue and green circles).

Original: We conjecture that a moregeneralizable knowledge is learned as more trainingepoch is performed (indicated by the samplesmix).
Revised: Conjecture: a more generalizable knowledge is learned during training (indicated by the samples mix).

Original: Although, it is slower to be adopted by the model in inferring OOD samples.
Revised: Although, this generalizable knowledge is slower to be adopted by the model in inferring OOD samples .

Original: 3.2 OOD vs in-distribution on  syntactic characteristics
Revised: 3.2 OOD vs in-distribution on tendency to adopt syntactic heuristics

Original: [Figure]
Revised: [Figure]

Original: For the second hypothesis, we ﬁnd the following observations: Correlation values  are relatively low in the plot forall samples.
Revised: For the second hypothesis, we find the following observations (refer to figure 2): • Observation: Correlation values ( ρ ( m 2 , µ Ei ) , ρ ( m 2 , µ Ej ( in − dist ) ) , ρ ( m 2 , µ Ej ( OOD ) ) ) are relatively low in the plot for all samples (a).

Original: This applies to all cases (train, in-distribution,and OOD test).
Revised: This applies to all cases (train, in-distribution, and OOD test).

Original: However, an obvious divergence is observed in the correlation between OOD samples conﬁdence score and the heuristics measure for the entailmentsamples.
Revised: However, an obvious divergence is observed in the ρ m µ

Original:  This observation conﬁrms our hypothesis in 2.2that models are more prone to adopt syntactic heuristicswhen making inference on OOD samples .
Revised: plot for the entailment Conjecture: This observation confirms our hypothesis in 2.2 that models are more prone to adopt syntactic heuristics when making inference on OOD samples .

Original:  All samples’ trends adhere to the per-label trends in thetrain and in-distribution test case  but contradict the OODcase.
Revised: • Observation: All samples’ trends adhere to the entailment trends in the train and in-distribution test case (orange and blue lines in figures (a) and (b)) but contradict the OOD case (green lines in figures (a) and (b)).

Original: In the OOD case, we can see that the negative andnon-zero slopes are only found in all samples trend, butthe correlation keeps increasing (with a huge difference invalue) in the entailment samples trend.
Revised: In the OOD case, we can see that the negative and nonzero slopes are only found in all samples trend, but the correlation keeps increasing (with a huge difference in value) in the entailment samples trend.

Original:  This might indicatethat knowledge used by the model to infer class labels isconsistent for all classes in train and in-distribution butconﬂicts in the OOD case .
Revised: Conjecture: This might indicate that knowledge used by the model to infer class labels is consistent for all classes in train and in-distribution but conflicts in the OOD case .

Original: More supplementary plots ofthis analysis (more experiments on different datasets andthe trend plots for non-entailment samples) can be foundin appendix A.4.
Revised: More supplementary plots of this analysis (more experiments on different datasets and the trend plots for non-entailment samples) can be found in appendix A.4.

Original:  Limitations and Future Directions
Revised:  Limitations and Future Directions

Original: This data-centric approach to understanding the difference between OOD and in-distribution samplesoffers an intuition of failure mode in OOD.
Revised: This data-centric approach to understanding the difference between OOD and in-distribution samples offers an intuition of failure mode in OOD.

Original: However, the results and approach presented in this paperhave some limitations.
Revised: However, the results and approach presented in this paper have some limitations.

Original: Although all NLI sets in our experiments came from different topic domains and had distinctcharacteristics, a more careful selection is needed to ensure maximum separation of in-distributionvs. OOD data distribution.
Revised: Although all NLI sets in our experiments came from different topic domains and had distinct characteristics, a more careful selection is needed to ensure maximum separation of in-distribution vs. OOD data distribution.

Original: Zhang et al.
Revised: Zhang et al.

Original: [28] highlights the problem with experiments setupwhere OOD regions can lie in support of the data distribution: high logit values assigned to certain
Revised: [37] highlights the problem with experiments setup where OOD regions can lie in support of the data distribution: high logit values assigned to certain

Original: OOD samples due to model estimation error.
Revised: OOD samples due to model estimation error.

Original: By ensuring this criterion is met, we can achieve a moreaccurate conﬁdence score, which in turn will improve our results in section 3.1.
Revised: By ensuring this criterion is met, we can achieve a more accurate confidence score, which in turn will improve our results in section 3.1.

Original: More controlled experiment is needed for results in section 3.2 to ensure that the correlation betweenconﬁdence and m2 is not spurious (i.e., there exists no unobserved confounding variable).
Revised: More controlled experiment is needed for results in section 3.2 to ensure that the correlation between confidence and m2 is not spurious (i.e., there exists no unobserved confounding variable).

Original: One wayto remedy this is by running the same measurements on random subsets and see if the correlationpattern still holds (i.e., randomized controlled experiments).
Revised: One way to remedy this is by running the same measurements on random subsets and see if the correlation pattern still holds (i.e., randomized controlled experiments).

Original: Future work may include remedies for the two mentioned limitations.
Revised: Future work may include remedies for the two mentioned limitations.

Original: Furthermore, investigating theroot cause of contradicting syntactic heuristics of OOD and in-distribution samples that the modelfails to predict correctly (section 3.1) is also an exciting direction.
Revised: Furthermore, investigating the root cause of contradicting syntactic heuristics of OOD and in-distribution samples that the model fails to predict correctly (section 3.1) is also an exciting direction.

Revised: It will also be exciting to see the effect of correcting for distribution mismatches [27, 10, 32] to the observations presented in this paper.

Original: Adding more syntactic heuristicsfor analysis (e.g., coreference, negation) can further corroborate the analysis and conjecture presentedin this paper.
Revised: To further corroborate the analysis and conjecture presented in this paper, the two possible directions can be: (i) adding more syntactic heuristics for analysis (e.g., coreference, negation), and (ii) setting up similar experiments on vision and other domains datasets.

Original: Finally, we hope that the insights provided by this data-centric view can motivate moreinformed development of OOD detection and generalization methods.
Revised: Finally, we hope that the insights provided by this data-centric view can motivate more informed development of OOD detection and generalization methods.

Original: A.1 Supplementary plot: OOD vs in-distribution on training dynamics information
Revised: A Appendix

Original: [Figure]
Revised: [Figure]

Original: [Figure] [Figure][Figure]
Revised: [Figure] [Figure]

Original: [Figure]
Revised: [Figure]

Original: Results presented are at the end of epoch 8 for MNLI training and the end of epoch 50 for RTEtraining.
Revised: Results presented are at the end of epoch 8 for MNLI training and the end of epoch 50 for RTE training.

Original: This is based on the epoch in which the training error has converged (around 0.02). [Figure] [Figure] [Figure]
Revised: This is based on the epoch in which the training error has converged (around 0.02)

Original: [Figure]
Revised: [Figure]

Original: This section shows plots for correlation between conﬁdence scores ( ˆ µ i ) of non entailment [-samplesand m
 [Figure] [Figure] [Figure]-]
Revised: This section shows plots for correlation between confidence scores ( ˆ µ i ) of non entailment [-samplesand m
 [Figure] [Figure] [Figure]-]samples and m

Original: [Figure]
Revised: [Figure]

Original: We also added another measure to quantify tendency to adopt lexical overlap heuristic.
Revised: We also added another measure to quantify tendency to adopt lexical overlap heuristic.

Original: We calculatedm 1 = | s 1 (cid:84) s 2 | | s 1 | .
Revised: We calculated m 1 = | s 1 (cid:84) s 2 | | s 1 | .

Original: Essentially, this measures how much percentage of words found in the premise ( s 1 )can also be found in the hypothesis ( s 2 ).
Revised: Essentially, this measures how much percentage of words found in the premise ( s 1 ) can also be found in the hypothesis ( s 2 )

Original: [Figure]
Revised: [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]