Which Spurious Correlations Impact Reasoning in NLI Models? A Visual
  Interactive Diagnosis through Data-Constrained Counterfactuals
Robin Chan Afra Amini Mennatallah El-Assady
We present a human-in-the-loop dashboard tailored to diagnosing potential
spurious features that NLI models rely on for predictions. The dashboard
enables users to generate diverse and challenging examples by drawing
inspiration from GPT-3 suggestions. Additionally, users can receive feedback
from a trained NLI model on how challenging the newly created example is and
make refinements based on the feedback. Through our investigation, we discover
several categories of spurious correlations that impact the reasoning of NLI
models, which we group into three categories: Semantic Relevance, Logical
Fallacies, and Bias. Based on our findings, we identify and describe various
research opportunities, including diversifying training data and assessing NLI
models' robustness by creating adversarial test suites.