This paper describes our approach to the SemEval-2024 safe biomedical Natural Language Inference for Clinical Trials (NLI4CT) task, which concerns classifying statements about Clinical Trial Reports (CTRs). We explored the capabilities of Mistral-7B, a generalist open-source Large Language Model (LLM). We developed a prompt for the NLI4CT task, and fine-tuned a quantized version of the model using an augmented version of the training dataset. The experimental results show that this approach can produce notable results in terms of the macro F1-score, while having limitations in terms of faithfulness and consistency. All the developed code is publicly available on a GitHub repository

翻译：本文描述了我们在SemEval-2024“面向临床试验的安全生物医学自然语言推理”（NLI4CT）任务中的方法，该任务涉及对关于临床试验报告（CTR）的陈述进行分类。我们探索了Mistral-7B这一通用型开源大语言模型（LLM）的能力。我们为NLI4CT任务开发了提示词，并使用增强版的训练数据集对模型的量化版本进行了微调。实验结果表明，该方法在宏观F1分数方面能产生显著结果，但在忠实度和一致性方面存在局限。所有开发的代码已在GitHub仓库中公开提供。