  Safe and reliable natural language inference is critical for extracting
insights from clinical trial reports but poses challenges due to biases in
large pre-trained language models. This paper presents a novel data
augmentation technique to improve model robustness for biomedical natural
language inference in clinical trials. By generating synthetic examples through
semantic perturbations and domain-specific vocabulary replacement and adding a
new task for numerical and quantitative reasoning, we introduce greater
diversity and reduce shortcut learning. Our approach, combined with multi-task
learning and the DeBERTa architecture, achieved significant performance gains
on the NLI4CT 2024 benchmark compared to the original language models. Ablation
studies validate the contribution of each augmentation method in improving
robustness. Our best-performing model ranked 12th in terms of faithfulness and
8th in terms of consistency, respectively, out of the 32 participants.
