SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language Models on Natural Language Inference for Clinical Trials
<|reference_start|>SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language Models on Natural Language Inference for Clinical Trials: 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.<|reference_end|>
@article{aguiar2024seme,
    title={SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language
  Models on Natural Language Inference for Clinical Trials},
    author={Mathilde Aguiar, Pierre Zweigenbaum, Nona Naderi},
    journal={arXiv preprint arXiv:2404.03977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03977},
    primaryClass={cs.CL}
}