  The NLI4CT task at SemEval-2024 emphasizes the development of robust models
for Natural Language Inference on Clinical Trial Reports (CTRs) using large
language models (LLMs). This edition introduces interventions specifically
targeting the numerical, vocabulary, and semantic aspects of CTRs. Our proposed
system harnesses the capabilities of the state-of-the-art Mistral model,
complemented by an auxiliary model, to focus on the intricate input space of
the NLI4CT dataset. Through the incorporation of numerical and acronym-based
perturbations to the data, we train a robust system capable of handling both
semantic-altering and numerical contradiction interventions. Our analysis on
the dataset sheds light on the challenging sections of the CTRs for reasoning.
