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\title{UniBuc at SemEval-2024 Task 2: Tailored Prompting with Solar for Clinical NLI}

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\author{
Marius Micluța-Câmpeanu$^{\clubsuit,\heartsuit,}$\thanks{These authors contributed equally to this work.},
\mbox{   }\mbox{   } Claudiu Creangă$^{\clubsuit,\heartsuit,}$\footnotemark[1]\\
\textbf{\mbox{   }\mbox{   }Ana-Maria Bucur$^{\clubsuit,\heartsuit}$,}
\textbf{\mbox{   }\mbox{   }Ana Sabina Uban$^{\spadesuit,\heartsuit}\textbf{,} \mbox{   }\mbox{   }$
Liviu P. Dinu$^{\spadesuit,\heartsuit} \mbox{   }\mbox{   }$}  
 \\ 
   %$^\heartsuit$Human Languages Technologies Research Center, University of Bucharest 
  $^\spadesuit$ Faculty of Mathematics and Computer Science
   \\
     % $^\spadesuit$ Faculty of Mathematics and Computer Science, University of Bucharest \\
  $^\clubsuit$Interdisciplinary School of Doctoral Studies, $^\heartsuit$HLT Research Center \\
  University of Bucharest, Romania\\
  \small
{\tt marius.micluta-campeanu@unibuc.ro, claudiu.creanga@s.unibuc.ro}  \\
\small
  {\tt ana-maria.bucur@drd.unibuc.ro, \{auban, ldinu\}@fmi.unibuc.ro}\\  
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}
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\begin{document}
\maketitle
\begin{abstract}

This paper describes the approach of the UniBuc team in tackling the SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. We used SOLAR Instruct, without any fine-tuning, while focusing on input manipulation and tailored prompting. By customizing prompts for individual CTR sections, in both zero-shot and few-shots settings, we managed to achieve a consistency score of 0.72, ranking 14th in the leaderboard. Our thorough error analysis revealed that our model has a tendency to take shortcuts and rely on simple heuristics, especially when dealing with semantic-preserving changes.

\end{abstract}

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\section{Introduction}
\input{sections/introduction}

\section{Related Work}
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% Background

\section{Data and Task Description}
\input{sections/data}

\section{Methodology}
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\section{Results}
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\section{Conclusions and Future Work}
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\section*{Acknowledgements}
This work was partially supported by a grant on Machine Reading Comprehension from Accenture Labs and by the POCIDIF project in Action 1.2. ``Romanian Hub for Artificial Intelligence''.

\bibliography{semeval.bib}

\appendix

% \section{Appendix}
% \label{sec:appendix}

\section{Additional examples}
\label{sec:examples}

\subsection{Summary mismatch example}\label{sec:mismatch-example}

The Results section of the CTR with ID ``NCT00467844'' is the following:
\begin{quoting}[rightmargin=0em]
    Outcome Measurement:   The Efficacy of GTx-024 on Total Body Lean Mass.  Change in total body lean mass as measured by dual energy x-ray absorptiometry (DEXA)from baseline to 4 months.  Time frame: Baseline to Four Months.
    
    Results 1:   Arm/Group Title: GTx-024 1 mg  Arm/Group Description: [Not Specified]  Overall Number of Participants Analyzed: 32  Median (Full Range)  Unit of Measure: kg  1.55        (-2.06 to 12.64)
    
    Results 2:   Arm/Group Title: GTx-024 3 mg  Arm/Group Description: [Not Specified]  Overall Number of Participants Analyzed: 34  Median (Full Range)  Unit of Measure: kg  0.98        (-4.84 to 11.54)
\end{quoting}

The summary is conditioned on the following hypothesis: ``all patients treated with gtx-024 1mg in the primary trial gained lean body mass over a 10 year period''. However, other hypotheses are concerned with other quantities: ``at least one patient treated with GTx-024 1mg in the primary trial gained over 10 kilos of Lean body Mass''. For the latter, our summary is misleading because the ``10 kilos'' information is missing. This could be mitigated by refraining to summarize short sections.

\section{Prompt templates}
\label{sec:prompts}
\input{sections/prompts}

\section{Infrastructure}
\label{sec:infrastructure}
\input{sections/infrastructure}


\end{document}