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\title{iML at SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials with LLM Based Ensemble Inferencing }

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% \author{Abbas\ Akkasi^1, Adnan\ Khan^1, Mai\ A.\ Shaaban^2, Mohammad\ Yaqub^2, Majid\ Komeili^1 \\
%         ^1School\ of\ Computer\ Science, Carleton\ University,\ Ottawa, Canada \\^2Mohamed\ bin\ Zayed\ University\ of\ Artificial\ Intelligence,\ Abu\ Dhabi,\ UAE}
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%         \And
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%         % Author 2 \\ Address line \\ ... \\ Address line \And
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%        , Mohammad Yaqub,  Majid Komeili  \\ Mohamed bin Zayed University of Artificial Intelligence\\ Abu Dhabi, UAE
%         }
\author{
 \textbf{Abbas Akkasi\textsuperscript{1}},
 \textbf{Adnan Khan\textsuperscript{1}},
 \textbf{Mai A. Shaaban\textsuperscript{2}},
 \textbf{Majid Komeili\textsuperscript{1}},
\\
 \textbf{Mohammad Yaqub\textsuperscript{2}},
%  \textbf{Sixth Author\textsuperscript{1}},
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\\
\\
 \textsuperscript{1}School of Computer Science Carleton University, Ottawa, Canada\\
 \textsuperscript{2} Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, UAE
 % \textsuperscript{3}Affiliation 3,
 % \textsuperscript{4}Affiliation 4,
 % \textsuperscript{5}Affiliation 5
\\
 \small{
   \textbf{Correspondence:} \href{mailto:email@domain}{abbasakkasi@cunet.carleton.ca}
 }
}
\begin{document}
\maketitle
\begin{abstract}
The task of textual entailment holds significant importance when dealing with clinical data, as it serves as a foundational component for extracting and synthesizing medical information from vast amounts of unstructured text. 
% In the clinical context, textual entailment involves determining whether a piece of text, such as a patient's health record, implies another text, like a clinical guideline or a diagnostic criterion. 

To investigate the consistency with which Natural Language Inference (NLI) models capture semantic phenomena critical for intricate inference within clinical NLI  contexts, SemEval$-$2024 has organized a shared task focused on  NLI for Clinical Trials (NLI4CT). This task provides participants with a dataset annotated by humans for the purpose of model training and requires the submission of the results on test data for evaluation.
% Furthermore, a secondary aim of this shared task is to explore the capacity of clinical NLI models to conduct faithful reasoning.
We engaged in this shared task2 at SemEval$-$2024, employing a diverse set of solutions, with a particular emphasis on leveraging a Large Language Model (LLM) based  zero-shot inference approach to address the challenge.
\end{abstract}
\input{Introduction.tex}
\input{Background.tex}
\input{Model.tex}
\input{latex/table_results}
\input{Experiments.tex}
\input{Results.tex}
\input{Conclusion.tex}

\section{Acknowledgments}
We extend our sincere gratitude to Dana Osama and Anees Hashmi for their valuable cooperation and contributions to this work. In addition, this work received support from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Digital Alliance of Canada, to whom the authors extend their gratitude.
\bibliography{custom}

\end{document}
