GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective
Year: 2023

Abstract:
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase. However, the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks, limiting the real-world deployment of these methods. This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it. The benchmark includes 15 publicly available datasets for OOD testing, and evaluations are conducted on 8 classic NLP tasks over 21 popularly used PLMs. Our findings confirm the need for improved OOD accuracy in NLP tasks, as significant performance degradation was observed in all settings compared to in-distribution (ID) accuracy.

heading: 1 Introduction
text: Pre-trained Language Models (PLMs) (Qiu et al., 2020; Bommasani et al., 2021) have achieved competitive performance across standard NLP benchmarks (Blasi et al., 2022), such as GLUE (Wang et al., 2019b) and SuperGLUE (Wang et al., 2019a). However, recent studies (Gururangan et al., 2018; Ribeiro et al., 2019; Kaushik et al., 2020; Ribeiro et al., 2020; Ruder, 2021) show concerns that models are yet not close to achieving proper natural language understanding, essential questions being raised about their robustness (Srivastava et al., 2020; Wang et al., 2021b) and underlying sensitivity to systematic biases (Niven and Kao, 2019; Sagawa et al., 2020). Such issues manifest
∗ Equal contribution. Random order. Shuibai Zhang finished this work at Westlake University as an intern.
†{yanglinyi, zhangyue}@westlake.edu.cn Yue Zhang is the corresponding author.
in the performance decay, especially for out-ofdistribution (OOD) generalization when the test distribution differs from training (Arora et al., 2021; Malinin et al., 2021; Hupkes et al., 2022).
OOD generalization has been systemically studied for Computer vision (CV) and artificial general intelligence (AGI) (Koh et al., 2021; Srivastava et al., 2022), for which large evaluation datasets are available. While sharing the same aspirational goal, existing evaluations (Kaushik and Lipton, 2018; Min et al., 2019; Gardner et al., 2020) and methods (Hendrycks et al., 2020; Bommasani et al., 2021) for OOD generalization of NLP contains only one or a few tasks (Wu et al., 2021; Wang and Culotta, 2021; Howard et al., 2022; Lu et al., 2022), which do not adequately capture limitations of existing models, resulting in inflated test accuracy (Tu et al., 2020; Ribeiro et al., 2020). Thus it remains a gap in evaluating models in a unified way by executing a range of text classification tasks.
To facilitate research in this direction, we introduce the GLUE-X benchmark for evaluating the out-of-distribution performance of PLMs. GLUEX expands upon previous multi-task benchmarks (Zheng et al., 2022; Xu et al., 2020, 2021) by including test data from multiple domains, covering eight standard tasks in GLUE, with an average 2 test domains for each task, allowing comprehensive cross-distribution evaluations. Specifically, GLUE-X focuses on domain generalization, where a model trained on a source domain can be directly generalized to target domains without any labeled or unlabeled data from target domains. It also enables the analysis of two main factors affecting the cross-domain generalization performance, namely the pre-trained language model (e.g., architecture, size, etc.) and different training strategies (e.g., fine-tuning, prompt-tuning (Chen et al., 2022), linear probing (Wu et al., 2020), and domaingeneralization training (Wang et al., 2023)).
Using GLUE-X, we evaluate the performance
12731
of 21 pre-trained language models in a unified setting and under the same experimental conditions. In addition, we consider 3 tuning strategies designed for improving single-source domain generalization: linear probing (Tripuraneni et al., 2020; Wu et al., 2020), fine-tuning, and the linear probing then fine-tuning method (LP-FT) (Kumar et al., 2022). Finally, we analyze the internal causes of OOD robustness at the feature level by measuring the rationale overlap between human and model predictions (Lei et al., 2016).
Results show that the average accuracy of PLMs on cross-domain evaluations falls significantly short of human performance, even for the highestperforming model (80.1% – human versus 74.6% – model). In contrast to the GLUE leaderboard, where over 20 single-model results outperform human baselines, none of the backbones included in GLUE-X is able to surpass human performance under the same evaluation setting. These findings suggest the importance of cross-distribution evaluation for natural language processing. In addition, evidence shows that the superior performance of PLMs on GLUE may be relatively superficial and less useful as a performance indicator in practice.
Detailed analysis shows that (1) no one backbone can significantly outperform the others across all tasks, which is consistent with the conclusion (Wenzel et al., 2022) in the computer vision; (2) surprisingly, the influence of model architectures is somehow more significant than the model parameters towards the OOD robustness; (3) the ID and OOD performance holds a linear correlation in most cases for text classifications; (4) in terms of the tuning strategy, we show that linear probing and then fine-tuning can slightly improve the OOD performance compared to standard fine-tuning.
To our knowledge, we are the first to systemically evaluate natural language understanding systems for cross-distribution generalization on genuine data compared to human performance. More importantly, we make datasets of cross-domain evaluations for all typical text classification tasks, which allows us to report OOD results under the same experimental conditions. We open-source the codebase and datasets 1. The GLUE-X leaderboard is available at https://gluexbenchmark.com/.
1https://github.com/YangLinyi/GLUE-X

heading: 2 Related Work
text: Benchmarking Robustness to OOD. Recent work (Ibrahim et al., 2022) finds that today’s leading PLMs are not robust to changing domains, where some OOD test samples varied during training. In particular, pre-trained transformers can rely heavily on spurious patterns (artefacts) (Gururangan et al., 2018; Kaushik et al., 2020; Tu et al., 2020). For this reason, the standard heldout accuracy can overestimate the performance (Sagawa et al., 2020; Kaushik et al., 2021), and evaluating the OOD robustness is crucial for realworld applications, which require models to hold good transferability. Consequently, there is a rising concern about improving dataset and benchmark development. Recent work introduces new outof-distribution benchmarks for graphs (Gui et al., 2022), optical character recognition (OCR) (Larson et al., 2022), computer vision (CV) (Ibrahim et al., 2022), time series tasks (Gagnon-Audet et al., 2022), and artificial general intelligence (AGI) (Koh et al., 2021; Srivastava et al., 2022). However, evaluating the out-of-distribution generalization in a multi-task setting has received relatively little attention for NLP.
There is a line of work focusing on the development of challenge datasets, representing as Adversarial NLI (Nie et al., 2020), Dynabench (Kiela et al., 2021), Contrastive Set (Gardner et al., 2020), and AdvGLUE (Wang et al., 2021a) where examples are created to be difficult for current models via an iterative, adversarial, and human-and-modelin-the-loop procedure. However, these datasets focus on robustness and stability issues rather than generalization and the artifact. In contrast, GLUEX contains both off-the-shelf and self-collected datasets to implement cross-distribution tests.
Prior work (Wenzel et al., 2022) observed that OOD performance holds a linear correlation with ID accuracy in CV based on 172 publicly available datasets and 31k networks, while their relationship is largely dataset-dependent. However, this conclusion has been found somewhat controversial, as Teney et al. (2022) argue that the selection of datasets influences the OOD performance.
Existing Benchmarks for NLU. There have been different types of leaderboards towards evaluating natural language understanding (NLU) systems. Examples of building challenging benchmarks in recent years include GLUE (Wang
et al., 2019b), SuperGLUE (Wang et al., 2019a), FewGLUE (Schick and Schütze, 2020), FEVER (Petroni et al., 2021), FewNLU (Zheng et al., 2022), and AdvGLUE (Wang et al., 2021a). In particular, FewGLUE and FewNLU focus on the fewshot learning challenge. The performance decay of NLP models has been found in real-world deployment because of the arises of the OOD generalization challenge as well as robustness issues, such as adversarial robustness. Similar to our work, other benchmarks, such as AdvGLUE (Wang et al., 2021a), leverage the training set extracted from GLUE for each task. Differently, we consider evaluating OOD performance in a general multi-task setting, where the test data arise from one or more different distributions.
Domain Generalization (DG) (Wang et al., 2022a) aims to learn a generalized model that is robust to unseen distributions using training data from multiple domains (Balaji et al., 2018; Dou et al., 2019; Vu et al., 2022; Varshney et al., 2022). We focus on the single-source DG (Huang et al., 2020; Krueger et al., 2021; Wang et al., 2022a) setting, which is a popular setting for measuring the OOD robustness in NLP (Hendrycks et al., 2020), and aligns with the GLUE leaderboard. As stated by a recent taxonomy and review towards generalisation research in NLP (Hupkes et al., 2022), current work does not provide standardized data or procedures for generalization testing, while we use GLUE-X as the first attempt towards this goal.

heading: 3 Data and Settings
text: The goal of GLUE-X is to provide an extension of GLUE with the same training data but multifarious OOD test sets.

heading: 3.1 Overview of GLUE-X
text: The evaluation in GLUE-X is intrinsically related to the domain generalization task considering a practical and challenging setting, where a model trained on multiple source domains can be directly generalized to a target domain without any labeled or unlabeled data from the target domain (Muandet et al., 2013). We articulate the following tasks and datasets in GLUE-X. Tasks. As a benchmark styled after GLUE (Wang et al., 2019b), we consider eight tasks in GLUE-X: Sentiment Analysis (SST-2), Natural Language Inference (MNLI, QNLI), Textual Entailment (RTE),
Paraphrase (MRPC, QQP), Textual Similarity (STSB) and Linguistic Acceptability (CoLA). 2 Datasets. GLUE-X follows the same in-domain training data and evaluation metrics as GLUE (Wang et al., 2019b). To construct the out-ofdomain test, we adopt popular datasets extracted from different domains while keeping the same prediction labels as the original tasks in GLUE. The detailed data statistics are shown in Table 1.

heading: 3.2 Dataset Curation
text: We construct test sets for each task under the requirement that they share the same label types with the training set. To this end, GLUE-X contains 15 OOD datasets, including publicly available datasets (Amazon, HANS, etc) and newly collected/re-constructed datasets (Grammar Test). In particular, we select the OOD datasets for each task, including sentiment analysis – IMDB (Maas et al., 2011), Yelp (Zhang et al., 2015), Amazon (Kaushik et al., 2020), and Flipkart (Vaghani and Thummar, 2023); linguistic acceptability – Grammar Test; textual similarity – SICK (Zhang et al., 2018); NLI – MNLI-Mismatched (Williams et al., 2017), SNLI (Bowman et al., 2015), and SICK (Zhang et al., 2018); Textual Entailment – RTE; Paraphrase – MRPC and QQP (Bentivogli et al., 2009; Dolan and Brockett, 2005; Wang et al., 2017;
2The WNLI task is not included in GLUE-X since there is no sufficient in-domain data for constructing OOD tests (Wang et al., 2022b; Yang et al., 2019, 2022).
McCoy et al., 2019). Regarding the QNLI task, we convert instances from NewsQA (Trischler et al., 2017) to the consistent data format of QNLI for conducting the OOD evaluation. The detailed description of the newly collected dataset, Grammar Test, can be found in Appendix A.
SICK contains multiple labels, including textual similarity, also used as an OOD test set of the textual similarity task. We rounded floating number labels of textual similarity to integers from 0 to 4, converting it into a five-class dataset to align with other classification tasks in GLUE-X. In addition, MRPC and QQP are leveraged as OOD datasets of each other as the paraphrasing task.

heading: 3.3 Metrics
text: We first average metrics to get a score for those tasks with multiple metrics. Following GLUE and SuperGLUE, we then report the score of NLU models by averaging the scores of all tasks as the OOD performance. For rankings, in addition to the robustness rank by considering the decreased ratio between OOD and ID performance, we adopt Friedman rank (Friedman, 1940) over multiple tasks:
rankF = 1
n
n∑
i=1
ranki, (1)
where n is the number of tasks (e.g., n = 8 in Table 3) and ranki is the rank of the performance in the i-th task considering in the GLUE-X. We report the robustness ranking in terms of the decreased ratio of OOD performance and Friedman rank.

heading: 3.4 Post-hoc Analysis
text: In addition to quantitative analysis, we choose two tasks, sentiment analysis, and natural language inference, for post-hoc feature analysis (Lei et al., 2016). We adopt the sensitivity of contextual decomposition technique (Jin et al., 2019; Yang et al., 2021), which removes part of inputs from the sequence text to evaluate a model’s sensitivity to them, thereby allowing for identifying important features. The output is the overlap between rationales by models and humans, which to some extent represents the trust of models (Jacovi and Goldberg, 2020; Yang et al., 2020).
Formally, given a phrase p starting with the negative limitations in the k-th document D(k), we sample the documents which contain the same phrase p to alleviate the influence by chance when there
are multiple shreds of evidence saturating the prediction. The window size of the phrase p is limited to 3. Taking sentiment analysis for example, given “This movie was so unbelievably bad” if we only remove the non-causal word movie, the prediction is not expected to change for a robust model.
The importance score is computed as follows:
ϕ(p, D̂(k)) = ED̂(β) [l ( D̂(β); D̂ ) −
l ( D̂(β)\p; D̂ ) ]
(2)
where D(β) denotes the resulting text after masking out a single token (phrase) starting with the negative pronoun (un-, non-, etc.) in the length of N surrounding the phrase p. We use l ( D̂(β)\p; D̂ )
to represent the model prediction logits of the ground-truth class after replacing the masked-out context. \p indicates the operation of masking out the phrase p in a given document.

heading: 3.5 Models and Training Strategies
text: Models. To ensure that our results are relevant for both researchers and practitioners, we consider both top-performing model backbones and costefficient methods: Discriminative Models – BERTbase, BERT-large (Devlin et al., 2018), RoBERTabase, RoBERTa-large (Liu et al., 2019), XLNetbase, XLNet-large (Yang et al., 2019); Generative Models – BART-base, BART-large (Lewis et al., 2020), T5-small, T5-base, T5-large (Raffel et al., 2020), GPT2, GPT2-medium, GPT2-large (Radford et al., 2019); Generative and Discriminative Models – ELECTRA-small, ELECTRAbase, ELECTRA-large (Clark et al., 2020); CostEfficient Models – ALBERT-base (Lan et al., 2020), and DistilBERT-base (Sanh et al., 2019). We also report the results of GPT-3 (Brown et al., 2020) and GPT-3.5 (OpenAI, 2023) through in-context learning. We follow the official implementations of several pre-trained language models from Huggingface3 to reproduce results on GLUE using the validation set and test these models on GLUE-X.
3https://huggingface.co/models
The hyper-parameters of each model are selected by using grid search and can be found in Appendix. Fine-tuning Strategies. We investigate the efficacy of different fine-tuning strategies for OOD generalization. In particular, we consider three paradigms: standard fine-tuning, fine-tuning only the head (linear probing), and linear probing then fine-tuning. The detailed training cost and inference speed estimated by a single V100 are shown in Table 2, in which we evaluate the performance using the in- and out-of-domain test data, recording the training cost in GLUE and GLUE-X. We use 50 NVIDIA Tesla V100 GPU cards and 8 NVIDIA A100 GPU cards and spend 10,000+ GPU hours based on the estimation with a single V100 card.

heading: 4 Experiments
text: We explore the facets of OOD generalization in NLP using GLUE-X, highlighting discrepancies to previous findings and discussing their implications.

heading: 4.1 Human Annotation
text: We employ human annotators to give predictions on OOD datasets and identify rationales. Predictions. We use a crowd-sourcing company to recruit editors and annotators to give predictions on 15 OOD datasets. To fairly compare human performance with models, we simulate the models’ OOD testing during the manual annotation process. Specifically, annotators are given essential instructions and a few examples from the in-domain dataset that gently guide them to annotate. Then they are asked to label instances from unseen OOD datasets, typically collected from other domains. 1,000 testing samples are used to obtain the human performance for each OOD dataset.
We employ multiple labelers to annotate the same data point (1,000 samples for each dataset) during the annotation to ensure the high quality of the crowdsourcing work. All annotators have an undergraduate degree in English or a PhD in an English-speaking country. In particular, we employ ten people to annotate the SICK dataset as same as the original data (Zhang et al., 2018). We employ two annotators for labeling the same instance for the other datasets. After the trial phase of data annotation, we set the Inter-Annotator Agreement (IAA) score threshold for each task depending on the difficulty level. Finally, the average IAA over the 15 OOD datasets is 0.857, indicating acceptable agreement.
Rationale Marking. Following Kaushik et al. (2020) and Kaushik et al. (2021), we use extractive explanations for marking rationales that support classification decisions. Inspired by Kaushik et al. (2021) and Lertvittayakumjorn and Toni (2021), we leverage the rationale marking annotated by humans to compare with rationale selected by models on sentiment analysis and natural language inference (NLI) tasks. We ask two labelers to annotate sampled instances from IMDB, Yelp, and Amazon datasets for the sentiment analysis task. At the outset, annotators were given instructions and examples that gently guided them to annotate rationales. Only adjectives, adverbs, nouns, and verbs were considered rationale candidates. Besides, rationales were required to carry complete semantic information. We sampled 6,000 instances for each dataset randomly. Using F1 score, the IAA for IMDB, Yelp and Amazon are 0.874, 0.871, and 0.840, respectively. For NLI, we use the explanation dataset, e-SNLI (Camburu et al., 2018), to assert the models’ trust.

heading: 4.2 Prediction Results
text: Overall Performance on GLUE-X. We report the average score of different models sorted in descending order representing the overall performance in Table 3. In addition to the overall performance, we provide the Friedman Rank for in- and out-ofdomain results. From Table 3, we observe that all pre-trained models involved in GLUE-X show significant performance decay under the OOD test compared to the ID performance (20.05% decay in average). The results also suggest no significant difference in the OOD robustness between generative and discriminative models for text classification. We also provide the results of GPT-3 with in-context learning in Appendix G since it leverages a different training strategy. Model-level Analysis. On the model level, we observe that ELECTRA-large achieves the best performance for both ID (89.18%) and OOD (74.62%) tests. Lightweight models, BERT-base, GPT-2, and DistilBERT-base, are in the bottom three on GLUEX with the lowest OOD performance. In contrast, the base-size ELECTRA and ALBERT achieve comparable generalization results. Moreover, by comparing the Friedman rank of OOD and ID tests in Table 3, we observe that the fluctuation of the OOD F-rank is slightly lower than the ID F-rank, which hints that the uncertainty of performance
has been decreased on GLUE-X by using a large amount of the test data. The Performance of Compressed Models. The results of GLUE-X suggest that OOD generalization still faces fundamental challenges, especially for lightweight models. For example, we find that compressed models (e.g., DistilBERT-base) show relatively low performance compared to others. Differently, the OOD performance of ALBERTbase (11M parameters) is significantly higher than DistilBERT-base (65.30% vs. 61.94%), even better than several moderate-sized models (BERTlarge, GPT2-medium, and XLNet-base).

heading: 4.3 Discussion
text: Human vs. Model. The average performance decay between in- and out-of-domain tests of humans (87.10% – ID vs. 80.14% – OOD) is significantly lower than models, even for the best-performing model with the lowest performance decay (7.82% vs. 16.33%), as shown in Table 4. Regarding the average OOD performance, the human baseline is also much higher than the models, with at least an 6.69% increase (80.14% vs. 74.62%)4. Such
4Note that the human performance of RTE, MRPC, and QQP is still adjusted and will be updated in the next version.
a large performance gap indicates that PLMs cannot achieve competitive results with humans on GLUE-X. More specifically, the human baseline outperforms the state-of-the-art results on five of eight tasks. It is noteworthy that we control OOD evaluations of humans in the same experimental setting with models by testing on unseen samples.
OOD Robustness. As shown in Table 4, we suggest that there is no silver bullet towards the OOD robustness, given that no single model can consistently outperform others over all tasks on GLUE-X. For example, ELECTRA-large can only achieve the best performance on four of eight tasks. We also find that the generalization for the CoLA dataset is the most challenging task for models since the test set holds the biggest difference with training data. In contrast, models tend to perform better on the relatively easy dataset, such as sentiment analysis (SST-2). For example, the best-performing model ELECTRA-large can achieve a 94.67% accuracy on SST-2 yet only a 37.85% Matthew’s Corr on CoLA. Besides, we also observe that the distribution shift between the ID and OOD datasets largely influences the OOD generalization results. In particular, the performance decay on the OOD test is exacerbated by the increase of distribution shifts,
Model SST-2 MNLI QNLI RTE MRPC QQP STS-B CoLA Avg AvgOOD OOD OOD OOD OOD OOD OOD OOD OOD ∆↓
as shown in Appendix C. Model Architectures vs. Parameter Size. The rightmost column of Table 4 demonstrates the decreased ratio representing the model robustness to some extent. Although the large-sized model, such as T5-large, and RoBERTa-large, can surpass the corresponding base-sized models in terms of the lower decreased ratio, empirical evidence from Table 4 also shows that model types could be more influential than the parameter size towards the OOD performance. Specifically, as shown in Table 4, results of the same architecture with different parameters are closer to the results of similar parametersize models based on different architectures. For instance, the decreased ratio of T5 architectures pretraining with different parameter sizes (T5: 16.98% – large (737M); 18.47% – base (223M); 18.57% – small (61M)) are close to each other, similar to RoBERTa (18.46% –large vs. 19.40% – base). It hints that designing model architectures and training methods could be one of the future directions for improving OOD robustness. Robustness vs. Trust. The average results of the rationale overlap between models and humans for three sentiment analysis tasks are shown in Table 5, indicating the trust measurement. As shown in the table, somehow surprisingly, we find that the bestperforming discriminative model – RoBERTa-large
(see Table 3) achieves the lowest rationale overlap between humans and models. While RoBERTalarge can achieve a relatively high overlap with humans on the NLI task (see Appendix D). This can be because the rationale overlap is largely influenced by datasets.
It is noteworthy that small-sized models can achieve relatively higher rationale overlaps than
large-sized models, which is generally consistent with the results reported by previous work (DeYoung et al., 2020). For instance, ELECTRA-small achieves the highest F1 score with only 13.48M parameters. In addition, the models pre-trained with the same architectures usually achieve similar performance (e.g., ELECTRA-small and ELECTRAlarge, GPT2-medium and GPT2-large).
ID vs. OOD Performance. We show the correlation of three tasks between the in- and out-ofdomain results in Figure 1 (the full results can be found at Appendix F). Unsurprisingly, we observe that the in-domain performance is always higher than the out-of-domain performance. Specifically, we find that the OOD performance is much lower than the ID performance in the task of COLA. In contrast, the gap between ID and OOD performance based on SST-2 and MNLI is relatively lower than others. We suppose this is partially influenced by the distribution shift between the inand out-of-domain datasets.
Regarding the type of pre-trained models, we show that discriminative models show a stronger linear correlation when compared to generative models (19 data points). From the task perspective, we observe that datasets largely influence the correlation between ID and OOD. For instance, ID and OOD performance are inversely correlated on MRPC yet almost correlated on other tasks, hinting that the inverse correlation is possible for the specific task when the size of test samples is limited.
The Influence of Tuning Methods. Taking MNLI as an example, we compare the results of RoBERTa-base using three different training strategies in Figure 2. As found by previous work (Kumar et al., 2022), fine-tuning can do worse than linear probing in the presence of a large distribution shift in CV. However, as shown in Figure 2, we find that linear probing methods show relatively low accuracy for both ID and OOD tests, which is different from the conclusion in CV. This can be because freezing pre-trained features hinders the generalization of NLP tasks that are generally more complex than the OOD generalization in CV. While the LP-FT can be relatively helpful for improving the OOD robustness of NLP models in terms of the slight performance improvement compared to the standard fine-tuning method. For this reason, there is still much room to improve in designing methodologies of domain generalization that can improve the OOD robustness for text classification. In addition to tuning methods discussed in GLUE-X, the recently emerging trend of the development of large-scale language models (LLMs) represented by ChatGPT is worth paying attention to. In particular, how to appropriately define the OOD generalization for LLMs is still under-explored since the
pre-training corpus of these models is not disclosed yet (Wang et al., 2023).

heading: 5 Conclusion
text: We constructed GLUE-X, an OOD robustness benchmark for natural language understanding tasks that aim to enable fair evaluation over multiple datasets from multiple domains in a consistent setting. With GLUE-X, we evaluate 21 pre-trained models on 8 classification tasks, providing analysis using 3 different tuning strategies and posthoc analysis for gaining internal causes for the OOD robustness. We conclude that (1) current PLMs still have a lag much behind human-level towards the OOD robustness; (2) the ID and OOD performance usually hold a linear correlation in most cases, while the coefficiency of the correlation is primarily related to the selection of OOD datasets; (3) stronger architectures can bring decent performance benefit, especially for the OOD performance.
Limitation
Our primary focus is on the OOD robustness of text classification tasks. However, there are other NLP tasks that the community should not ignore. GLUE-X currently does not include language generation tasks such as machine translation, summarization, and dialogue. Moreover, extending the current GLUE-X to more real-world datasets from different domains is of great importance. We aim to make GLUE-X a continuously maintained project.
Ethics Statement
This paper honors the ACL Code of Ethics. Public available datasets are used to establish the GLUEX leaderboard. No private data was used. All annotators from the crowdsourcing company have received enough labor fees corresponding to their amount of annotated instances. The code and data are open-sourced under the CC-BY-NC-SA license.

heading: Acknowledgement
text: We acknowledge with thanks Wei Zhou from Zhejiang University, who help us build the website, as well as the many others who have helped. We would also like to thank anonymous reviewers for their insightful comments and suggestions to help improve the paper, especially for Reviewer 2. This publication has emanated from research conducted
with the financial support of the Pioneer and “Leading Goose” R&D Program of Zhejiang under Grant Number 2022SDXHDX0003 and the 72nd round of the Chinese Post-doctoral Science Foundation project 2022M722836. Yue Zhang is the corresponding author.

heading: A Data Collection
text: We derive the CoLA-OOD dataset from the Public High School English Exam, which contains 304,277 examples. The original multi-choice fill-in tests are converted into COLA-style, with correct answers as positive examples and incorrect answers as negative examples. The golden answer is given by the English teacher who is a native speaker or holds an English Teaching degree. We collect the data from publicly available internet resources, and the original open-access materials can be found from https://www.koolearn.com/shiti.
The input of the CoLA-OOD dataset, Grammar Test, is a text span containing a QA pair or a few sentences. The ground truth of the output is to decide whether the grammar of the sentence is acceptable or not. For example, given the sentence ‘Is there a post office near here? Yes, there isn’t .’, the label is unacceptable since there is a grammar error existing in the input. Otherwise, for a sentence without any grammar errors, ‘The young man is the CEO of the company, In other words, he is in charge of the company.’, the corresponding label is acceptable.

heading: B Training Details
text: We illustrate the cross-domain evaluation settings used for GLUE-X in Figure 3. Notably, the source domain only contains a single dataset, while target domains can include more than one dataset from multiple domains.
Regarding the training, we performed the grid search for each task, kept the best-performing checkpoint in ID datasets, and tested their performance on their corresponding OOD datasets. The hyperparameters used by these weights can be seen in Table 9.

heading: C Domain Distributions
text: We evaluate distribution shifts between different datasets regarding Maximum Mean Discrepancy
(MMD) and Word Overlap Rate. MMD distance focuses on the semantic distribution shift between datasets, while Word Overlap Rate pays more attention to superficial similarity.
C.1 Word Overlap
The similarity between datasets of In-distribution datasets and Out-of-distribution datasets are shown in Figure 4.
C.2 MMD Distance
The MMD distance between ID and OOD datasets is shown in Figure 5 for each task including in GLUE-X. When computing the MMD distance between two datasets, we ensure that the same number of sentences are sampled and fed into PLMs (e.g. RoBERTA-base) to extract their semantic features. We sample multiple times to get an average MMD sample score to estimate MMD distance of two datasets. The calculation of MMD is shown as follows:
MMD2[F , X, Y ] = 1 m(m− 1)
m∑ i ̸=j k (xi, xj)
+ 1 n(n− 1) n∑
i ̸=j k (yi, yj)−
2
mn
m,n∑
i,j=1
k (xi, yj)
(3) F is a MMD function class, i and j represents the batch of instances sampled from different distributions. m and n are the size of i and j.

heading: D Rationale Overlap
text: In order to measure the difference between rationales detected by PLMs and humans, we define precision as the percentage of the predicted rationales that also exist in the human annotation and recall as the percentage of words in the human annotation that also exist in the predicted rationales. We calculate the F1 score as an evaluation metric of overlap.
We show the evaluation of rationale overlap between models and humans on the e-SNLI dataset (Camburu et al., 2018) in Table 6. We find that the performance gap between different models is not very large (varying from 30.93 to 34.98). Models show a higher rationale overlap with humans
based on e-SNLI than sentiment analysis datasets. This can be because the average length of instances in e-SNLI is generally shorter than that in sentiment analysis datasets. In particular, the basesized ELECTRA has achieved the highest F1 score (34.98%) among these models.

heading: E The In-domain Evaluation Results
text: Following (Wang et al., 2019b), we report the indomain evaluation results in Table 8. We generally find that ELECTRA-large achieves the best average performance over seven tasks. Note that we report the results by evaluating models on the validation set provided by GLUE.

heading: F The Correlation between ID and OOD Performance
text: In general, we find that the overall performance of ID and OOD tests shows a linear correlation for both discriminative and generative models. In addition to the overall performance, we look at task-level performance at a more granular level in Figure 6. As shown in Figure 6, we find that the linear correlation does not exist for every task. For example, the linear correlation is extremely weak for MRPC and QQP, with relatively low OOD accuracy. While the linear correlation becomes significant on STSB and QNLI.

heading: G The ID and OOD Performance of GPT-3 and GPT-3.5
text: Settings. The performance of GPT-3 and GPT-3.5 is shown in Table 7, where we report the classification results based on 1,000 instances for each task. The training strategy of GPT-3 is simulated to keep the same as human evaluation. We feed the model with some in-domain instances as instructions before testing on the OOD dataset. To achieve this, we adopt the official API for calculating the indomain performance of GPT-3 (text-DaVinci-003) based on 1,000 sampled ID instances. We leverage the in-context learning following (Ouyang et al., 2022) to calculate its OOD results on GLUE-X. Results. In Table 7, it is interesting to see that the performance decay ratio of GPT-3 caused by the domain generalization is similar to GPT-3.5 while significantly larger than Humans (11.49% – GPT 3.5 vs. 11.51% – GPT-3 vs. 7.82% – Humans), indicating that there is much room for improvement in the OOD robustness, even for state-of-the-art LLMs. Meanwhile, it can be seen that the OOD performance of GPT-3.5 is still far behind the human performance (66.90% – GPT-3 vs. 80.14% – Humans), and slightly lower than ELECTRA-large (69.68%). Notably, the results of GPT-3/3.5 should be treated with caution and just for reference because we are not sure if datasets of GLUE-X are already included in the training corpus of GPT3/3.5. Also, the OOD performance listed in Table 7 cannot be compared with PLMs fairly, as we only adopt instructions to evaluate it not fine-tuning the model like other PLMs in GLUE-X.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: Six.

heading: 3 A2. Did you discuss any potential risks of your work?
text: Seven.

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: One.
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? Left blank.

heading: 3 B1. Did you cite the creators of artifacts you used?
text: Section 3.

heading: 3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: Section 3.

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 3.

heading: 3 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section 3.
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank.
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3.
C 3 Did you run computational experiments? Section 4.

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Section 3 (Table 2).
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Appendix.

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4.

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3.
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? Section 4.

heading: 3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Section 4.

heading: 3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Section 4.

heading: 3 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to
text: crowdworkers explain how the data would be used? Section 4.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

heading: Future Work
text: It hints that designing model architectures and training methods could be one of the future directions for improving OOD robustness. Robustness vs. Trust. The average results of the rationale overlap between models and humans for three sentiment analysis tasks are shown in Table 5, indicating the trust measurement. As shown in the table, somehow surprisingly, we find that the bestperforming discriminative model – RoBERTa-large
(see Table 3) achieves the lowest rationale overlap between humans and models. While RoBERTalarge can achieve a relatively high overlap with humans on the NLI task (see Appendix D). This can be because the rationale overlap is largely influenced by datasets. It is noteworthy that small-sized models can achieve relatively higher rationale overlaps than
large-sized models, which is generally consistent with the results reported by previous work (DeYoung et al. , 2020). For instance, ELECTRA-small achieves the highest F1 score with only 13. 48M parameters. In addition, the models pre-trained with the same architectures usually achieve similar performance (e. g. , ELECTRA-small and ELECTRAlarge, GPT2-medium and GPT2-large). ID vs. OOD Performance. We show the correlation of three tasks between the in- and out-ofdomain results in Figure 1 (the full results can be found at Appendix F). Unsurprisingly, we observe that the in-domain performance is always higher than the out-of-domain performance. Specifically, we find that the OOD performance is much lower than the ID performance in the task of COLA. In contrast, the gap between ID and OOD performance based on SST-2 and MNLI is relatively lower than others. We suppose this is partially influenced by the distribution shift between the inand out-of-domain datasets. Regarding the type of pre-trained models, we show that discriminative models show a stronger linear correlation when compared to generative models (19 data points). From the task perspective, we observe that datasets largely influence the correlation between ID and OOD. For instance, ID and OOD performance are inversely correlated on MRPC yet almost correlated on other tasks, hinting that the inverse correlation is possible for the specific task when the size of test samples is limited. The Influence of Tuning Methods. Taking MNLI as an example, we compare the results of RoBERTa-base using three different training strategies in Figure 2. As found by previous work (Kumar et al. , 2022), fine-tuning can do worse than linear probing in the presence of a large distribution shift in CV