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.

翻译：SemEval-2024的NLI4CT任务旨在利用大语言模型开发针对临床试验报告的自然语言推理鲁棒模型。本届任务特别引入了针对临床试验报告数值、词汇与语义层面的干预措施。我们提出的系统融合了最先进的Mistral模型与辅助模型，专注于NLI4CT数据集的复杂输入空间。通过引入基于数值和缩写的扰动数据，我们训练出能够处理语义改变与数值矛盾两种干预措施的鲁棒系统。对数据集的深入分析揭示了临床试验报告中制约推理能力的关键部分。