This paper describes our submission to Task 2 of SemEval-2024: Safe
Biomedical Natural Language Inference for Clinical Trials. The Multi-evidence
Natural Language Inference for Clinical Trial Data (NLI4CT) consists of a
Textual Entailment (TE) task focused on the evaluation of the consistency and
faithfulness of Natural Language Inference (NLI) models applied to Clinical
Trial Reports (CTR). We test 2 distinct approaches, one based on finetuning and
ensembling Masked Language Models and the other based on prompting Large
Language Models using templates, in particular, using Chain-Of-Thought and
Contrastive Chain-Of-Thought. Prompting Flan-T5-large in a 2-shot setting leads
to our best system that achieves 0.57 F1 score, 0.64 Faithfulness, and 0.56
Consistency.

本论文通过在临床试验报告中应用自然语言推理模型进行的一项多证据文本蕴含（NLI）任务，描述了基于微调和合并遮蔽语言模型以及使用模板的大型语言模型（如类比思维链和对比类比思维链）的两种不同方法的测试。使用辅助 Flan-T5 large 模型在 2 次迭代的情况下，实现了最佳系统，得分为 0.57 F1，0.64 忠实度和 0.56 一致性。