SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language
  Models on Natural Language Inference for Clinical Trials
Mathilde Aguiar Pierre Zweigenbaum Nona Naderi
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.