\section{Conclusion}\label{conclusion}
In conclusion, our participation in NLI4CT-2024 involved proposing an ensemble approach that incorporated multiple decision-makers, with two Large Language Models (LLMs) serving as foundational models. We explored various data preparation techniques, including abstractive summarization and similarity-based sentence filtering, for use in both prompting and classification contexts. The comparable performance of the prompt-based model to the overall ensemble model, coupled with its significant outperformance of the classification models, underscores the substantial potential of pre-trained generative foundation models in solving similar problems. We posit that the application of instruction tuning and the incorporation of domain-specific data could markedly enhance the results.