Assessing Out-of-Domain Language Model Performance from Few Examples

While pretrained language models have exhibited impressive generalization capabilities, they still behave unpredictably under certain domain shifts. In particular, a model may learn a reasoning process on in-domain training data that does not hold for out-of-domain test data. We address the task of predicting out-of-domain (OOD) performance in a few-shot fashion: given a few target-domain examples and a set of models with similar training performance, can we understand how these models will perform on OOD test data? We benchmark the performance on this task when looking at model accuracy on the few-shot examples, then investigate how to incorporate analysis of the models’ behavior using feature attributions to better tackle this problem. Specifically, we explore a set of factors designed to reveal model agreement with certain pathological heuristics that may indicate worse generalization capabilities. On textual entailment, paraphrase recognition, and a synthetic classification task, we show that attribution-based factors can help rank relative model OOD performance. However, accuracy on a few-shot test set is a surprisingly strong baseline, particularly when the system designer does not have in-depth prior knowledge about the domain shift.


Introduction
The question of whether models have learned the right behavior on a training set is crucial for generalization.Deep models have a propensity to learn shallow reasoning shortcuts (Geirhos et al., 2020) like single-word correlations (Gardner et al., 2021) or predictions based on partial inputs (Poliak et al., 2018), particularly for problems like natural language inference (Gururangan et al., 2018;McCoy et al., 2019) and question answering (Jia and Liang, 2017;Chen and Durrett, 2019).Unless we use eval- uation sets tailored to these spurious signals, accurately understanding if a model is learning them remains a hard problem (Bastings et al., 2021;Kim et al., 2021;Hupkes et al., 2022).
This paper addresses the problem of predicting whether a model will work well in a target domain given only a few examples from that domain.This setting is realistic: a system designer can typically hand-label a few examples to serve as a test set.Computing accuracy on this small set and using that as a proxy for full-test set performance is a simple baseline for our task, but has high variance, which may cause us to incorrectly rank two models that achieve somewhat similar performance.We hypothesize that we can do better if we can interpret the model's behavior beyond accuracy.With the rise of techniques to analyze post-hoc feature importance in machine-learned models (Lundberg and Lee, 2017;Ribeiro et al., 2016;Sundararajan et al., 2017), we have seen not just better interpretation of models, but improvements such as constraining them to avoid using certain features (Ross et al., 2017) like those associated with biases (Liu and Avci, 2019;Kennedy et al., 2020), or trying to more generally teach the right reasoning process for a problem (Yao et al., 2021;Tang et al., 2021;Pruthi et al., 2022).If post-hoc interpretation can strengthen a models' ability to generalize, can they also help us understand it?
Figure 1 illustrates the role this understanding can play.We have three trained models and are trying to rank them for suitability on a new domain.The small labeled dataset is a useful (albeit noisy) indicator of success.However, by checking model attributions on our few OOD samples, we can more deeply understand model behavior and analyze if they use certain pathological heuristics.Unlike past work (Adebayo et al., 2022), we seek to automate this process as much as possible, provided the unwanted behaviors are characterizable by describable heuristics.We use scalar factors, which are simple functions of model attributions, to estimate proximity to these heuristics, similar to characterizing behavior in past work (Ye et al., 2021).We then evaluate whether these factors allow us to correctly rank the models' performance on OOD data.
Both on synthetic (Warstadt et al., 2020), and real datasets (McCoy et al., 2019;Zhang et al., 2019), we find that, between models with similar architectures but different training processes, both our accuracy baseline and attribution-based factors are good at distinguishing relative model performance on OOD data.However, on models with different base architectures, we discovering interesting patterns, where factors can very strongly distinguish between different types of models, but cannot always map these differences to correct predictions of OOD performance.In practice, we find probe set accuracy to be a quick and reliable tool for understanding OOD performance, whereas factors are capable of more fine-grained distinctions in certain situations.
Our Contributions: (1) We benchmark, in several settings, methods for predicting and understanding relative OOD performance with few-shot OOD samples.(2) We establish a ranking-based evaluation framework for systems in our problem setting.(3) We analyze patterns in how accuracy on a few-shot set and factors derived from token attributions distinguish models.

Motivating Example
To expand on Figure 1, Figure 2 shows an in-depth motivating example of our process.We show three feature attributions from three different models on an example from the HANS dataset (McCoy et al., 2019).These models have (unknown) varied OOD performance but similar performance on the indomain MNLI (Williams et al., 2018) data.Our task is then to correctly rank these models' performance on the HANS dataset in a few-shot manner.
We can consider ranking these models via simple metrics like accuracy on the small few-shot dataset, where higher-scoring models are higher-ranked.However, such estimates can be high variance on small datasets.In Figure 2, only M3 predicts nonentailment correctly, and we cannot distinguish the OOD performance of M1 and M2 without additional information.
Thus, we turn to explanations to gain more insight into the models' underlying behavior.With faithful attributions, we should be able to determine if the model is following simple inaccurate rules called heuristics (McCoy et al., 2019).Figure 2 shows the heuristic where a model predicts that the sentence A entails B if B is a subsequence of A. Crucially, we can use model attributions to assess model use of this heuristic :we can sum the attribution mass the model places on subsequence tokens.We use the term factors to refer to such functions over model attributions.
The use of factors potentially allows for the automation of detection of spurious signals or shortcut learning (Geirhos et al., 2020).While prior work has shown that spurious correlations are hard for a human user to detect from explanations (Adebayo et al., 2022), well-designed factors could automatically analyze model behavior across a number of tasks and detect such failures.
In this section, we formalize the ideas presented thus far.Token-level attribution methods (a subset of post-hoc explanations) are methods which, given an input sequence of tokens x def = x 1 , x 2 , ..., x n and a model prediction ŷ def = M (x) for some task, assign an explanation φ(x, ŷ) def = a 1 , . . ., a n where a i corresponds to an attribution or importance score for a corresponding x i towards the final prediction.
For cases where the model, prediction, and inputs are unambiguous, we abbreviate this simply We assume that the model is trained on an indomain training dataset D T and will be evaluated on some unknown OOD set D O .Given two models M 0 and M 1 , with a small amount of data D (O,t) ⊂ D O (t = 10 examples or fewer in our settings), our task is to predict which model will generalize better.We break the process into 2 steps (see Figure 2): 1. Hypothesize a heuristic.First we must identify an underlying heuristic H that reflects pathological model behavior in the OOD dataset.For example, the subsequence heuristic in Figure 2 corresponds to a heuristic which always predicts entailed if the hypothesis is contained within the premise.Let h(M i ) abstractly reflect how closely the ith model's behavior aligns with H. Let s(M i ) be the true OOD performance of model M i .If we then assume that h(M i ) faithfully models some pathological heuristic H, we should have that h(M 0 ) > h(M 1 ) > . . .> h(M m ) implies s(M 0 ) < s(M 1 ) < . . .< s(M m ) .In other words, the more a model M i agrees with a pathological heuristic H, the worse it performs.
2. Measure alignment.We now want to predict the ranking of s(M i ); however, with few labeled examples there may be high variance in directly evaluating these metrics.We instead use factors f (x, φ i ) which map tokens and their attributions for model M i to scalar scores that should correlate with the heuristic H. Factors can be designed to align with known pathological heuristics, where higher scores indicate strong model agreement with the associated heuristic.We then estimate the ranking of s(M i ) using the relative ranking of the corresponding h(M i ) approximated through factors.
Concretely, to measure the alignment, we first compute for each input x j ∈ D (O,t) the prediction M i (x j ) and the explanation φ(x j ) for that prediction.These φ(x j ) are used to compute the score f (x j , φ(x j )) for model M .We take the overall score of the model to be ), the mean over the t examples in D (O,t) .We then directly rank models on the basis of the F (i) values: the higher the average factor value (the more it follows the heuristic), the lower the relative ranking: F (0) > F (1) =⇒ s(M 0 ) < s(M 1 ).Therefore we can sort the models by these values and arrive at a predicted ranking.We later also consider factors which to not intuitively map to specific heuristics.
Baselines We also consider three principle explanation-agnostic baselines.A natural baseline given D (O,t) is to simply use the accuracy (ACC) on this dataset: this may be noisy on only a few examples, and frequently leads to ties. 1e can also assess model confidence (CONF), which looks at the softmax probability of the predicted label, as well as looking at CONF-GT which only looks at the softmax probability of the groundtruth label.

Models Compared
In this work, we compare various models across different axes yielding different D O performance.The first approach we use is inoculation (Liu et al., 2019a), which involves fine-tuning models on small amounts or batches of D O data alongside in-domain data to increase model performance on OOD data.The second approach we use is varying the model architecture and pre-training (e.g., using a stronger pre-trained Transformer model).
In Section 5, we use inoculation to create 5 RoBERTa-base (Liu et al., 2019b) models of varying D O performance for each of the three MSGS sets.In Section 6 where we consider the HANS and PAWS datasets, we inoculate a variety of models.For HANS, we inoculate 5 RoBERTa-large models.We additionally examine DeBERTa-v3-base (He et al., 2021b,a) and ELECTRA-base (Clark et al., 2020) models fine-tuned on in-domain MNLI data.For PAWS, we inoculate 4 RoBERTa-base models on the in-domain D T set.We also inoculate ELECTRA-base and DEBERTA-base models.We include complete details for these models in Appendix A. The generated models represent a realistic problem scenario: a practitioner may have many different models with similar D T performance, but different D O performance.We specifically crafted suites of models which have both near pairs (models with similar D O performance) and far pairs.

Attribution Methods
We experiment using several token-level attributions methods: LIME (Ribeiro et al., 2016) computes attribution scores using the weights of a linear model approximating model behavior near a datapoint.SHAP (Lundberg and Lee, 2017) is similar to LIME, but uses a procedure using Shapley values.Finally, Integrated Gradients (TOKIG) (Sundararajan et al., 2017) compute φ i by performing a line integral over the gradients with respect to token embeddings on a path from a baseline token to the ground truth token; commonly, this baseline token is chosen to be <MASK>.While intuitively sensible, Harbecke (2021) has voiced concerns regarding the use of TOKIG in NLP.

Evaluation Setup
Because model ranking using a small D (O,t) may be unstable, we conduct all experiments over a number of different sampled D (O,t) sets.We first sample M examples from each set (in the range of 200-600), then generate explanations for all models on each example.We then take 400-500 bootstrap samples of size n (we report results for n = 10, as experimental results were similar for sizes 5 and 20), simulating many few-shot evaluations.For each bootstrap sample, we analyse m 2 model pairs.Details can be found in Appendix B.
We define a "success" as a technique correctly ranking a model pair, when measured by D O performance (on the full set); otherwise is a "failure".We define pairwise accuracy as the accuracy for a method ranking a particular model pair across all bootstrap samples.We define few-shot accuracy (or just accuracy) as the average of the pairwise accuracies over the m 2 model pairs.By reporting ranking accuracy across a diverse set of models, we ensure a comprehensive evaluation.

MSGS: A Proof of Concept
We first show experiments on the Mixed Signals Generalization Set (MSGS) dataset presented in Warstadt et al. (2020)

as a proof of concept
The man sat on the table.
3) A7ribu:ons on : C O H m 0 t q 1 d q 2 X 9 s 5 t 2 x 9 5 / t t p W n b N P 3 o R 9 8 A T g b 7 T e < / l a t e x i t > We consider three of their linguistic features: MORPH (presence of an irregular past verb like "drew"), ADJECT (prescence of an adjective), and VERB (if the main verb is an -ing verb), each paired with the surface feature of "the" being in the sentence.
We design factors which look at attributions on the tokens corresponding to these linguistic features, including the tokens surrounding these features as well to account for feature dependence on surrounding words.Our factor f (x, φ) = − m+2 i=(m−2) φ(x i ), where m is the index of the feature-critical word for that dataset (e.g., "slept" for IRREG) and φ(x i ) is the attribution at an index.This factor corresponds closely to the heuristic that the dataset was designed for, or alternately, we can see this factor as inversely proportional to what other information the model is using (that is, information outside of this window).We name the factors IRREG, VERG, and ADJ for the MORPH, VERB, ADJECT sets respectively.Note that this approach assumes that a system designer has prior knowledge of the relevant linguistic and surface feature.This is a generous assumption, and for this dataset is almost sufficient to formulate the rule used to construct it, hence why we call this a proof of concept.We will show more realistic conditions in Section 6.
Models To create a suite of models with varying D O performance, we inoculate following the steps outlined in Section 4.1.We evaluate our factors via accuracy as described in Section 4.3.More details about the inoculation is present in Section A of the appendix.

Results
Table 1 shows the results on this dataset.Our ACC baseline performs well: when models differ greatly in performance (e.g., one gets 50% and another gets 90% on the D O ), accuracy on the small D (O,t) ranks these correctly even despite the small subset size.The high regularity of the dataset also means that a model's behavior does not vary greatly from example to example, further reducing variance.However, this ranking is nevertheless still not perfect.We see that CONF performs very poorly, by contrast, showing that confidence is not helpful for measuring model behavior.
Overall, we see that methods using explanations are able to beat the ACC baseline, with the exception of TOKIG.We additionally found trends within the explanation techniques themselves, with LIME reliably performing the best, and TOKIG being the worst.But generally, all techniques can offer relevant information, and in the best case, the attributions can tell us more reliably what a model is learning than evaluation on a small set of D (O,t) data can.In Section 6, we investigate if these results generalize to real-world datasets.

Realistic OOD Settings
We now consider two datasets corresponding to realistic OOD settings treated in past work.
First, HANS (McCoy et al., 2019) targets spurious heuristics within MNLI (Williams et al., 2018), such as the hypothesis being a subsequence of the premise, with balanced test sets that can be used to detect model reliance on these heuristics.Models following these heuristics always predict entailed for the hypotheses, and will perform at random chance accuracy on the dataset.We use MNLI as our in-domain training set in this setting.
Second, PAWS (Zhang et al., 2019) is a paraphrase identification task.PAWS-QQP is an OOD dataset for Quora Question Pairs (QQP) (Iyer et al., 2017) that is composed of pairs with swapped content words/phrases (e.g., I ran from the Grand Canyon to California to I ran from California to the Grand Canyon).A paraphrase model that relies heavily on lexical overlap will not be sensitive to these changes, and will always predict the label of y = 1 to indicate paraphrase.We use QQP as our in-domain training set in this setting.
Details regarding models used in this section are presented in Section 4.1.From the test sets of the corresponding datasets, we randomly sample 400 examples from PAWS and 600 from HANS-CON and HANS-SUB each for use in bootstrap sampling, as detailed in Section 4.3.Information regarding the datasets considered can be found in Table 9.

Factors
General Factors Both HANS and PAWS involve comparing two sequences a, b of tokens, unlike MSGS which is classification over a single sequence.We can define our input x = a 1 , a 2 , ...a n , b 1 , b 2 , ..., b m as composed of these two sequences a and b with respective attributions φ a , φ b .We evaluate a number of factors that generally target sensitivity to both sequences and their differences, which represent a broad class of potential heuristics.

MAX-DIFF:
The difference between maximum attribution in a and b, i.e. max(φ a ) − max(φ b ).SUM-DIFF: the difference of the sum of attributions, i.e. n i=1 φ a,i − m i=1 φ b,i .

INDEX-DIFF:
The difference of attributions between shared words in a and b.FIRST-TOK: The attribution at the the first <SEP> token.
We explicitly note that this is the exhaustive set of factors we experimented with, not a cherry-picked set, in order to provide a comprehensive view of what does and doesn't work.We crafted these by manually examining attribution patterns on various datasets rather than trying a large number and keeping the best ones.

HANS Factors
We look at the "subsequence" heuristic discussed in Section 2 and the constituent heuristic, which assumes that the premise entails all complete subtrees in its parse-tree.For the subsequence OOD set (HANS-SUB) we note that the INDEX-DIFF factor, which specifically examines tokens in the shared subsequence, captures the setting's pathological heuristic.On the constituent OOD set (HANS-CON) we evaluate a factor that examines the attribution on the control words of the premise.For example, for the premise "Unless the doctors ran, the lawyers encouraged the scientists" and the hypothesis "The doctors ran", we would consider the attributions on the word "Unless".

PAWS Factors
We further investigate two intuitive heuristics that are based on the construction of the OOD set.SWAP-AVG uses the average attribution across all swapped tokens and SWAP-MAX-DIFF subtracts the highest magnitude attribution of swapped tokens in the first sentence and the highest magnitude attribution of swapped tokens in the second sentence.For example, for the pair ("What factors cause a good person to become bad ?", "What factors cause a bad person to become good ?"),SWAP-AVG would consider the attributions on "good" and "bad".SWAP-MAX-DIFF is analogous.

Inoculated Results
We first evaluate models that differ primarily through inoculation, as described in Section 4.1.Results are shown using SHAP in Table 2 which we selected through experiments in this setting as being the best performing.The conclusions here differ somewhat from those on MSGS.We note that the ACC baseline remains strong, while CONF is near random.We find that certain attribution factors are able to outperform the ACC baseline, with SWAP-AVG the best on PAWS (91.4%),INDEX-DIFF the best on HANS-SUB (91.3%), and CONST on HANS-CON (87.1%).This shows that even in these settings more realistic than MSGS, the right choice of factor reveals meaningful information about model generalization.Moreover, the heuristics that work well are those hand-designed for these datasets, confirming our hypothesis that measuring association with a heuristic via a factor may reveal something about performance.
We qualify these results by noting that in a true few-shot setting, there is some uncertainty regarding whether a chosen factor is truly the best one.As a coarse option, we find ACC to be reliable.However, these high-performing factors would still be useful in conjunction with accuracy, or if we had previously validated a factor as ranking models well and we wanted to apply it to rank new mod-els in this domain; the factors will generalize to new models even if they do not generalize to new datasets necessarily.

Architectural Change Results
We further examine our approach when ranking the performance of different pre-trained models (RoBERTa, ELECTRA, and DeBERTa).
Table 3 shows that a heuristic GUESS based on the expectation across choosing a best model and then randomly guessing consistently with that, gives a strong baseline of 72%.Factors also seem to do well in this setting, with all of the general heuristics outperforming the very low ACC baseline.
This suggests that in few-shot factors are able to capture distributional information that baselines can't.However, to qualify this, given that each set only compares between 3 pairs of models, it's easier for factors to happen upon strong accuracy patterns by chance.
Thus, in Table 4, we analyze this further by showing resuts on some individual model pairs.(R1, R2 are RoBERTa; E is Electra; D is DeBERTa) and D-R2, have different architectures, but similar OOD accuracy (see Table 6 in the Appendix).R1-D, E-D and E-R2 are different model types with more distant accuracy.Accuracy values for a single pair on this single dataset therefore only reflect differences across bootstrap samples.What we find in common across these types of pairings is that while some values are close to 50%, including the ACCURACY baseline, each column has several factors achieving very distinct (0%, 100%) accuracy values, consistently differentiating these models.As we note in Figure 4 (Appendix), this pattern of strong distinctions is quite common when different types of models are compared.We further discuss this in Section 7.

Analysis / Discussion
Accuracy is reliable, but factors can provide more fine-grained distinctions.On MSGS, where factors beat strong accuracy baselines, we notice that these pairwise accuracies are consistently high.For example, on the MORPH setting, for two models with 95% and 98% accuracy, our factors IRREG is 100% accurate, while the accuracy baseline here is only 58%, as test accuracy on D (O,t) does not discriminate well between two models with such close overall accuracy.
This holds at the fine-grained pairwise level as well.Figure 5 (also see Figure 6 in appendix) shows the baseline D (O,t) accuracy against a specific factor's accuracy for each model pair in MSGS.Each datapoint in the scatterplot represents a model pair and a point's vertical distance from the red line represents how much better or worse a given factor does compared to the baseline on a specific pair.We see a regular trend: explanations seem to systematically outperform the baseline across various pairs, with a few significant deviations for low-performing pairs.These results suggest that explanations can be useful and do add information otherwise missing from accuracy probing alone, especially when the underlying model architecture is held constant.With differing architectures (Figure 6), the problem is made more difficult, and selecting the right factor is less obvious; few-shot accuracy may be more reliable in this setting.Note, however, that these successes from any technique are in spite of us only inspecting 10 examples from the target domain.
Factors differentiate models strongly, though not always in a way aligned with OOD performance.Figure 4 and Table 4 both show that factors will often consistently decide in favor of a certain model regardless of the choice of D (O,t) , especially when dealing with models with different base architectures.Since ranking accuracy correlates with whether these strong alignments are consistent across a spectrum of models and choose the models with higher OOD performance, the tendency for factors to strongly favor a specific model doesn't necessarily correlate with strong overall performance, but does heavily imply that these factors extract meaningful information about the model from the attributions.Looking close at Table 4, we can see that that even between different model architectures, certain factors are more (INDEX-DIFF) or less (SWAP-MAX-DIFF) capable of making these distinctions.
Factors as projections of model feature space.Based on these results, we have evidence that the distributions of attributions are unique to models: in other words, a factor is like a scalar signature for a model's feature space with respect to some relevant features.Methods like inoculation, that change a model's behavior in direct ways lead to regular changes in that signature.In these cases, factors align with OOD performance, which explains why factors are so strong in our inoculated experiments.For our non-inoculated experiments (i.g.ELECTRA vs DeBERTa), the feature spaces are fundamentally different, so factor signatures will still capture these differences, but in a way less aligned with ranking on OOD performance.Future work may be able to expand on these differences and what they tell us beyond OOD performance.
Past work has also investigated using explanations to detect spurious correlations (Kim et al., 2021;Bastings et al., 2021;Adebayo et al., 2022).We are different in that we focus on ranking an array of models which exhibits different levels of generalization abilities, as opposed to giving a binary judgment of whether a model is relying on some shortcuts (Kim et al., 2021;Bastings et al., 2021;Adebayo et al., 2022).In addition, we experiment with tasks having nuanced shortcuts 'in the wild', contrary to synthetically constructed datasets in Bastings et al. (2021).In particular, Adebayo et al. (2022) study the usefulness of explanations in detecting unknown spurious features in an image classification task involving (realistic) possible shortcuts, but find that attributions are ineffective for detecting unknown shortcuts in practice.
We establish a robust framework for evaluation of fine-grained few-shot prediction of OOD performance, benchmarking approaches in this setting on a range of models.We find that accuracy is a reliable baseline, but intuitive attribution-based factors derived from explanations can sometimes better predict how models will perform in OOD settings, even when they have similar in-domain performance.We further analyze patterns of our approaches, discovering the potential for factors to represent views of model feature space, leaving further exploration to future work.

Limitations
There are a large number of explanation techniques and many domains these have been applied to.We focus here on a set of textual reasoning tasks like entailment where spurious correlations have been frequently identified.However, correlations in other settings like medical imaging (Adebayo et al., 2022) could yield different results.We also note that these datasets are all English-language and use English pre-trained models, so different settings may yield different results; additionally, our factors depend on how explanations are normalized between different examples.
Our paper and analysis themselves comment on the limitations of our methodology as well as explanations as a whole: we find that while explanations often can clearly distinguish different models, knowing which factors will do so, or guaranteeing that explanations align with OOD performance, remains difficult.

A Details of Inoculation
One of the methods we used to obtain models with different performances on the OOD sets was inoculation (Liu et al., 2019a) fine-tuning or further fine-tuning models on small amounts or batches of OOD data alongside indomain data to bring model performance on OOD sets up.
MSGS We borrow notation from Warstadt et al. (2020).Most of the fine-tuning data is ambiguous data that doesn't test the spurious correlation, but we add in small percentages of non-ambiguous data where the label favors either the surface or linguistic generalization, tilting the model in that direction.
HANS Specific innoculation results for RoBERTa-large are present in Table 6.We additionally use MNLI pre-trained ELECTRA and DeBERTa models from huggingface.These performance details are also located in Table 6.

B Bootstrapping Details
We now describe our process for bootstrapping and evaluating the capability of explanations in our setting.
For a sampled population of examples from the D O set, for the m models that we're examining at a time, we generate explanations for each of the m models on all of the sampled population.We then repeatedly take a sample with replacement (500 times) of 10 examples D O,t each, where we have 500 × 10 × m total explanations we want to examine.We calculate factors for each of the 10 explanations in each D O,t sample and pool them to get a list of factor metrics for the D O,t , one for each model.
For each pair, we then look at the ground-truth D O ranking for models and their respective factor Table 8: Tokig numbers for Table 3 metrics, getting successes where these match, and failures otherwise.When we average these accuracies across our 500 bootstrap samples, we get pairwise distributions (the distribution of successes vs failures on a sample for a given pair), which we can further aggregate to get few-shot accuracies.
Note, in practice, to prevent variance from runto-run, we fix the population of 500 D O,t s, but we validated that re-running on new sampled populations didn't impact any numbers greatly.Though we tried using several (5, 10, 20) D O,t sizes, we decided to use the probe size of 10 as a realistic probe size for our setting, which wouldn't be burdensome to hand-craft in practice.
Our methodology can be run quickly in a posthoc manner as many times as needed on top of a population of the necessary explanations.Table 9: Information regarding our considered datasets.For all datasets, the bootstrap sample size is fixed at 10.

C Additional Plots
Figure 4 shows additional information about the distrbution of pairwise accuracies between different model architectures.

D Reproducibility D.1 Computing Infrastructure
All experiments were conducted on a desktop with 2 NVIDIA 1080 Ti (11 GB) and 1 NVIDIA Titan Xp (12 GB).

D.2 Runtimes
For PAWS and MSGS fine-tuned models, we finetuned for roughly 1 GPU hour per model.Since HANS models were trained for very few steps, their training time is inconsequential.Generating attributions required for numerical evaluation took less than 6 GPU hours.

D.3 Dataset Details
We used datasets in the JSONL format.We simplified all our dataset settings to binary classification for simplicity, and used data directly from the downloads made available in the original papers.

Figure 1 :
Figure 1: Our setting: a system developer is trying to evaluate a collection of trained models on a small amount of hand-labeled data to assess which one may work best in this new domain.Can baselines / attributions help?

Figure 2 :
Figure2: Explanations generated on the same sample for HANS subsequence data models M1, M2, M3 (have ascending OOD performance).The factor (shaded underlines) from knowledge of the OOD allows us to in this example predict the model ranking.

Figure 3 :
Figure 3: Example from the MSGS train and OOD test sets.The training data conflates a surface and linguistic generalization as described in Warstadt et al. (2020), resulting in models that learn a range of behaviors.Direct evaluation OOD on small data can tell us this, but explanations can also differentiate which of the two patterns is learned and how strongly they are learned.

Figure 4 :Figure 5 :
Figure 4: Distributions of pairwise accuracies on PAWS SHAP non-inoculated, all model pairs (left for accuracy baseline, right for all factors).

Figure 6 :
Figure 6: SHAP pairwise factor compared to ACC for HANS-CON and PAWS.Each point represents a factor accuracy (y-axis) for a pair of models in comparison to ACC (x-axis) for the same pair.Points above the red y = x line represent factors outperforming the accuracy baseline.CONST and DIFF-SUM are for HANS-CON, SWAP-AVG and SWAP-MAX-DIFF are for PAWS

Table 1 :
Few-shot ranking accuracy metric results on D (O,t) for MSGS.IRREG, VERB, and ADJ are detailed in Section 5. † indicates statistically significant improvement over accuracy (paired bootstrap test: p < 0.05)

Table 3 :
Few-shot heuristic ranking performance on OOD samples for HANS/MNLI and QQP/PAWS, specifically when comparing non-inoculated models (SHAP explanations), where we take the mean of pairwise accuracies for 3 pairs (for 3 models) on each set.

Table 6 :
Architecture details for our experiments."Steps" indicates the number of gradient updates from the specified dataset that are applied to the model.For HANS models, performance is on HANS-SUB/HANS-CON.For all models, small batch sizes were used, with weight decay of 0.1.

Table 7 :
LIME version of Table3