Robustness Gym: Unifying the NLP Evaluation Landscape (2021)

Abstract: Despite impressive performance on standard benchmarks, natural language processing (NLP) models are often brittle when deployed in real-world systems. In this work, we identify challenges with evaluating NLP systems and propose a solution in the form of Robustness Gym (RG), a simple and extensible evaluation toolkit that unifies 4 standard evaluation paradigms: subpopulations, transformations, evaluation sets, and adversarial attacks. By providing a common platform for evaluation, RG enables practitioners to compare results from disparate evaluation paradigms with a single click, and to easily develop and share novel evaluation methods using a built-in set of abstractions. RG is under active development and we welcome feedback & contributions from the community.

## introduction advances in natural language processing (nlp) have led to models that achieve high test accuracy on independent and identically distributed (i.i.d.) data. however, analyses suggest that models are not robust to data corruptions @xcite , distribution shifts @xcite , and harmful data manipulations @xcite , and rely on spurious patterns @xcite . in practice, these vulnerabilities hinder deployment of trustworthy systems, as seen in public-use systems that were later revealed to be systematically biased, such as chatbots (stuart-ulin, 2018) and recruiting tools @xcite . while practitioners know of these problems, it remains common to evaluate solely on i.i.d. data. ideally, the goal of evaluation is to perform a broad assessment of a model's capabilities on the types of examples that it is likely to see when deployed. this process is complex for practitioners, since existing tools cater to a specialized set of evaluations for a task, and provide no clear way to leverage or share findings from prior evaluations. thus, current evaluation practices face two challenges: in response to these challenges, we introduce robustness gym (rg), a simple, extensible and unified toolkit for evaluating robustness and sharing findings (figure 1 ). rg users can: ## the landscape of evaluation tools we describe two challenges facing evaluation today, and situate them in the context of existing work. ## robustness gym we address the challenges highlighted in section 2 with robustness gym (rg). we describe how users can build evaluations in section 3.1, and version evaluations and report findings in section 3.2. figure 1 provides a visual depiction of the system design and workflow in rg, while python examples for rg are in tables 4, 5 and 6 of the appendix. ## related tools and work we highlight additional related work for evaluation and reporting, including work on interpretability. evaluation and error-analysis. tools for evaluation and error analysis support users in understanding where their models fail. in contrast to rg, existing tools support only a subset of evaluations and analyses. errudite @xcite , snorkel @xcite support subpopulations, textattack (morris et al., 2020) adversarial attacks, nlpaug @xcite transformations, and crosscheck @xcite , manifold @xcite focus on visualization and analysis for model comparison. interpretability. tools for interpretability enable a better understanding of model behavior. these tools serve complementary objectives to robustness gym, e.g., explaining why a model makes a certain prediction, rather than performing broad evaluations. tools include the recent language interpretability tool (lit) @xcite ), ibm's ai explainability 360 @xcite , allennlp interpret @xcite , inter-pretml @xcite , manifold @xcite , pytorch captum (narine kokhlikyan and reblitz-richardson), dice @xcite , what-if @xcite , fairvis @xcite , and fairsight @xcite . many of these tools focus on interactive visualization, which limits their scope to interpreting small numbers of examples and makes their use susceptible to subjectivity and selection bias. by contrast, robustness gym can scale to large datasets, while testbenches ensure reproducibility of analyses. ## conclusion we introduced robustness gym, an evaluation toolkit that supports a broad set of evaluation idioms, and can be used for collaboratively building and sharing evaluations and results. robustness gym is under active development and we welcome feedback and contributions from the community.