GLUE - X : Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective (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 13 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.

## introduction pre-trained language models (plms) @xcite have achieved competitive performance across standard nlp benchmarks @xcite , such as glue @xcite and superglue @xcite . however, recent studies @xcite @xcite @xcite show concerns that models are yet not close to achieving proper natural language understanding, essential questions being raised about their robustness @xcite and underlying sensitivity to systematic biases @xcite . such issues manifest in the performance decay, especially for out-ofdistribution (ood) generalization when the test distribution differs from training @xcite @xcite . ood generalization has been systemically studied for computer vision (cv) and artificial general intelligence (agi) @xcite , for which large evaluation datasets are available. while sharing the same aspirational goal, existing evaluations @xcite @xcite and methods @xcite for ood generalization of nlp contains only one or a few tasks @xcite @xcite , which do not adequately capture limitations of existing models, resulting in inflated test accuracy @xcite . 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. glue-x expands upon previous multi-task benchmarks @xcite @xcite 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 @xcite , linear probing @xcite , and domaingeneralization training @xcite ). using glue-x, we evaluate the performance 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 @xcite , fine-tuning, and the linear probing then fine-tuning method (lp-ft) @xcite . finally, we analyze the internal causes of ood robustness at the feature level by measuring the rationale overlap between human and model predictions @xcite . 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 @xcite 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/ . there is a line of work focusing on the development of challenge datasets, representing as adversarial nli @xcite , dynabench @xcite , contrastive set @xcite , and advglue @xcite 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, glue-x contains both off-the-shelf and self-collected datasets to implement cross-distribution tests. prior work @xcite 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 @xcite 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 @xcite , superglue @xcite , fewglue @xcite , fever @xcite , fewnlu @xcite , and advglue @xcite . 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 @xcite , 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) @xcite aims to learn a generalized model that is robust to unseen distributions using training data from multiple domains @xcite @xcite . we focus on the single-source dg @xcite @xcite setting, which is a popular setting for measuring the ood robustness in nlp @xcite , and aligns with the glue leaderboard. as stated by a recent taxonomy and review towards generalisation research in nlp @xcite , current work does not provide standardized data or procedures for generalization testing, while we use glue-x as the first attempt towards this goal. ## data and settings the goal of glue-x is to provide an extension of glue with the same training data but multifarious ood test sets. ## experiments we explore the facets of ood generalization in nlp using glue-x, highlighting discrepancies to previous findings and discussing their implications. ## conclusion 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.