\section{Introduction}
Clinical NLI is a specialized application of Natural Language Processing (NLP) that focuses on understanding and inferring information from text within the healthcare domain. It involves analyzing and drawing conclusions from clinical narratives, such as electronic health records (EHRs), doctor's notes, medical transcripts, clinical trials and other forms of medical documentation \cite{percha2022natural}. The goal of clinical NLI is to determine the logical relationship between premises and hypotheses (conclusions) in clinical text.  By inferring information from clinical text, NLI can assist healthcare providers in making informed decisions by providing evidence-based recommendations and alerts. In addition, clinical NLI can be used to identify patient cohorts for clinical trials or research studies by inferring patient eligibility based on inclusion and exclusion criteria mentioned in clinical records. Applications of clinical NLI are not limited to the ones mentioned and there are lots of other usages in which clinical NLI can be useful \cite{percha2021natural}. NLI for clinical trials faces unique challenges due to the complexity of medical language, the need for domain-specific knowledge, and the sensitivity and privacy concerns associated with health data. However, advancements in NLP and specifically Large Language Models (LLMs) are continuously improving the accuracy and applicability of clinical NLI, making it an increasingly valuable tool in the healthcare industry.

To foster collaboration and dissemination of novel insights within this field, SemEval 2024 \cite{jullien-etal-2024-semeval} has established a shared task exclusively devoted to clinical NLI. A publicly accessible dataset, annotated by humans, has been made available to facilitate the comparison of solutions proposed by different researchers.

To address the challenge, we developed an ensemble-oriented solution that combines various  Large Language Models (LLMs) based models within the framework of prompting and fine-tuned classification. Our primary goals were to first understand the comparative performance of generative models versus classification models. Subsequently, we explored whether the use of automatic summarization models to condense the premises would influence the efficacy of both classifiers and generative models. Ultimately, our approach sought to facilitate synergistic interactions among the different models, leveraging their respective strengths to mitigate individual inference limitations.

Nevertheless, despite conducting a variety of experiments that involved combining summarization, fine-tuning classifiers, prompting, and more, the results demonstrated a clear superiority of generative models in comparison to the others, even when used independently.

The remainder of this paper is organized as follows: Section \ref{pastwork} provides a brief review of related work. The proposed model and its constituent modules are detailed in Section \ref{Model}. Sections \ref{experiments} and \ref{results} discuss the experiments conducted and the corresponding results. Finally, we conclude the paper in Section \ref{conclusion}.