\section{Past Work}\label{pastwork}
% Clinical NLI, as a sub-field of NLP, focuses on the ability of computational models to understand and draw logical conclusions from complex medical texts. Despite the potential of NLI to enhance clinical decision-making and streamline healthcare operations, challenges persist due to the nuanced and highly specialized language used in medical documentation. 
Recent literature underscores the need for sophisticated models that can accurately capture the semantics of clinical narratives and support reasoning in line with medical knowledge. \citet{jullien-etal-2023-nli4ct}, introduced a shared task on  NLI for clinical trials (NLI4CT), providing a dataset of annotated clinical trials and inviting researchers to develop models to tackle the associated challenges. The shared task comprises two sub-tasks: Textual Entailment and Evidence Retrieval, each designed to advance the state of NLI systems within the clinical domain.

\citet{zhou2023thifly}, took part in the NLI4CT-2023 challenge, proposing a model that utilizes both sentence-level and token-level encoding to address the task at hand. Furthermore, they enhanced the model's overall performance by employing general (T5-based model) and domain-specific (SciFive) pre-trained LLMs.

\citet{kanakarajan}, conducted an evaluation of several instruction-tuned Large Language Models (LLMs) in a zero-shot setting and fine-tuned the best-performing instruction-tuned model (T5 family models). Their findings suggest that instruction-tuned models yield better results for datasets with limited training samples. Additionally, they explored the impact of various prompts on the overall performance of the model. \cite{vladika2023sebis} and \cite{chen2023ncuee}, both created a model based on an ensemble approach that combines various fine-tuned iterations of biomedical LLMs. These models are designed to extract evidence from clinical trial report premises to support textual entailment in specific statements.  \citet{wang2023knowcomp},  developed a system that utilizes prompts created by humans to gather information from statements, section titles, and clinical trials. They then fine-tune pre-trained language models on these prompted sentences, training the models to identify the inferential connections between the statements and the clinical trials. \citet{pahwa2023bphigh}, characterized the NLI task as a form of text pair classification and utilized the GPT-3 model to classify samples within the framework of few-shot prompting. This approach takes advantage of the semantic similarity between text samples and the examples provided for in-context learning.


\citet{dias2023team}, employed supervised contrastive learning to enhance the sentence pair representations in the Biomed RoBERTa model. They then fine-tuned a linear classifier built upon these improved representations to identify evidence and execute textual entailment classification for sentence pairs.

\citet{vassileva2023fmi}, introduced a two-tiered system to address the sub-tasks of NLI4CT-2023. Initially, the system employs a BERT-based classifier, supplemented by contextual data augmentation, to categorize evidence-statement pairs as relevant or irrelevant. Subsequently, leveraging the relevant segments of the clinical trial identified in the first stage, the system applies another BERT-based classifier to ascertain whether the relationship between the elements is one of entailment or contradiction.

\citet{volosincu2023fii}, illustrated that a transformer model pre-trained on biomedical data for the task of entailment relation in NLI4CT-2023 does not automatically outperform traditional approaches like CNNs. Nonetheless, their model exceeded the baseline system's performance and provided meaningful directions for future research on how the model's architecture can be developed further. 
