Evaluating Models' Local Decision Boundaries via Contrast Sets (2004.02709v2)

Abstract: Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities. We propose a new annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model's decision boundary, which can be used to more accurately evaluate a model's true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets---up to 25\% in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.

• The paper introduces a contrast set paradigm that exposes models' vulnerabilities by testing local decision boundaries with minimally altered inputs.
• The methodology is demonstrated on 10 diverse NLP datasets, including tasks such as reading comprehension, sentiment analysis, and syntactic parsing.
• The results highlight significant performance drops, urging a shift from standard test sets to more robust evaluation strategies.

Overview of Evaluating Models' Local Decision Boundaries via Contrast Sets

The paper "Evaluating Models' Local Decision Boundaries via Contrast Sets" discusses a methodological improvement for evaluating the performance of NLP models beyond standard test sets. The authors argue that traditional test sets lead to misleading evaluation due to systematic gaps and annotator biases, which allow models to perform well without truly capturing the desired linguistic capabilities.

Key Contributions

1. Contrast Set Paradigm: The authors propose a rigorous annotation method where dataset creators perturb test instances in minimal but significant ways, typically altering the gold label. These contrast sets are designed to provide a more localized view of a model's decision boundary, allowing for more accurate assessments of model performance related to intended linguistic phenomena.
2. Demonstration Across Diverse Datasets: The utility of contrast sets is demonstrated by applying this concept to ten varied NLP datasets, including tasks like reading comprehension (DROP), sentiment analysis (IMDb), and syntactic parsing (UD English). The results consistently show a marked decrease in performance on contrast sets compared to the original test sets, highlighting gaps in models' understanding.
3. Numerical Outcomes: In some instances, models' performances on contrast sets declined by as much as 25%, illustrating the gaps that exist when simple decision boundaries fail to capture complex linguistic nuances. Furthermore, contrast consistency—a measure of whether a model correctly classifies all examples in a contrast set—was substantially lower than the accuracy on original test sets, underscoring the challenge.

Implications and Future Directions

The implications of this work are significant for both theoretical and practical aspects of AI research. By focusing on local decision boundaries, researchers can better understand where current models are lacking and where future improvements are necessary. This methodology could lead to developing more sophisticated models capable of generalizing beyond known data artifacts.

From a practical standpoint, contrast sets promote the creation of high-quality test sets that truly reflect the complexity of real-world language understanding tasks. Adoption of this paradigm across new datasets could foster a better evaluation standard, setting a benchmark for future research.

The work also highlights potential future developments in AI, suggesting pathways for addressing the identified systematic gaps. Automated and intelligent approaches to creating contrast sets could be one area of further exploration.

Conclusion

The paper contributes a novel methodology for more accurately evaluating NLP models' capabilities. By focusing on contrast sets and the framework of local decision boundaries, the authors reveal significant deficiencies in existing models' performances. This work forms a foundation for future improvements in model evaluation, ensuring consistent progress towards true comprehension in NLP tasks. The research encourages dataset creators to adopt these methods for building more robust and insightful benchmarks.