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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.06648 |
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Table of Contents:
- Standard NLP benchmarks often fail to capture vulnerabilities stemming from dataset artifacts and spurious correlations. Contrast sets address this gap by challenging models near decision boundaries but are traditionally labor-intensive to create and limited in diversity. This study leverages large language models to automate the generation of diverse contrast sets. Using the SNLI dataset, we created a 3,000-example contrast set to evaluate and improve model robustness. Fine-tuning on these contrast sets enhanced performance on systematically perturbed examples, maintained standard test accuracy, and modestly improved generalization to novel perturbations. This automated approach offers a scalable solution for evaluating and improving NLP models, addressing systematic generalization challenges, and advancing robustness in real-world applications.