Analyzing and Mitigating Dataset Artifacts in Natural Language Inference Models Using ELECTRA
Himanshu Joshi · International Journal of Multidisciplinary Research and Growth Evaluation · 2024
This paper investigates the challenges posed by dataset artifacts in Natural Language Inference (NLI) models, focusing on ELECTRA, a state-of-the-art transformer model. Dataset artifacts such as hypothesis-only biases, lexical overlap issues, and frequent label imbalances significantly impact model generalization, leading to erroneous predictions. We propose and evaluate a range of strategies, including adversarial training, data augmentation, instance weighting, and artifact-aware regularization, to mitigate these issues. Extensive experimental results demonstrate up to a 6% improvement in robustness and generalization, providing valuable insights for creating artifact-resistant NLP models.