\section{Proposed Model}\label{Model}
In tackling the NLI4CT task, our approach involved the construction of an ensemble model that integrates the judgments of multiple distinct decision-makers. These decision-makers differ concerning the nature of input data they process, the foundational models they employ, and the methodologies they adopt for label determination. Figure \ref{fig:model} provides a comprehensive illustration of the proposed solution. 
\begin{figure*}[ht!]
    \centering
    \includegraphics[scale=0.19]{NLI4CT1.png}
    \caption{Ensemble Model Proposed}
    \label{fig:model}
\end{figure*}
Components of the ensemble pool were developed within the frameworks of classification or prompting, utilizing LLMs. For classification tasks, SciFive \cite{zhou2023thifly} was selected as the base model due to its exemplary performance in the NLI4CT-2023 task. To enhance the models' ability to assimilate information from the input data, we employed both extractive and abstractive summarization techniques. The abstractive summarization was conducted using the T5-large model \cite{2020t5} to condense the premises. For extractive summarization, the premises were initially segmented into individual sentences, after which those exhibiting lower semantic similarity to the hypothesis were  excluded.

The pre-trained SciFive model ingests the text summarized by T5 to generate the initial component of the ensemble pool. Subsequently, this model undergoes fine-tuning through two distinct methodologies utilizing the summarized data: comprehensive fine-tuning and parameter-efficient fine-tuning, the latter of which is facilitated by employing LoRA \cite{hu2021lora} to produce subsequent members of the ensemble pool.

The remaining decision-makers within the ensemble are derived by prompting generative LLM\footnote{OpenOrca-Platypus2-13B, which is an autoregressive language model that utilizes the Lllama 2 transformer architecture. It is tailored for a variety of general-use applications, including chat, text generation, and code generation. This model has undergone training with a diverse mix of datasets, focusing on STEM and logic-based content, and it incorporates a carefully selected portion of data from the GPT-4 dataset within the OpenOrca collection.

} in a zero-shot inference context, utilizing both the original input data and variously summarized inputs. The specific prompt employed for the model is delineated in Listing \ref{prompt}.
\lstinputlisting[label=prompt,caption=Prompt Template Used.,language=python,numbers=none]{pythonfile.py}

Ultimately, the final decision for the test samples were made using a weighted majority voting approach. The performance of models on \textit{practice\_test} set were used for the combination process.


