%Recent work on natural language inference for clinical trials studies a directly related problem to that of entailment which is addressed by this shared task: detection of clinical contradictions in publications from the medical domain \cite{makhervaks2023clinical}.  

% In the field of processing clinical trial data and assisting clinical trial development using language models, recent studies \cite{wang-etal-2023-autotrial,datta2024autocriteria} address the tasks of automatically assessing eligibility for clinical trials in an explainable manner using LLMs.

% In relation to the dominant methodologies used in recent approaches for this task (based on LLMs), including our own, and the suitability and safety of using LLMs for the medical domain, \citet{pal2023med} assess the prevalence of hallucinations in medical texts generated using various generative models. \citet{feng2023chard} assess the capabilities of LLMs to reason about medical data as a potential method to automatically generate explanations about health-related information. 
% The previous edition of the NLI4CT shared task at SemEval 2023 \cite{jullien-etal-2023-semeval} included a similar entailment task as well as an evidence selection task. Most submitted systems used pretrained language models, the majority of which were discriminative, with some generative LLM approaches. A small minority of approaches use ontologies and rule-based algorithms. 

Recent work on clinical trial analysis includes detecting contradictions in medical publications \cite{makhervaks2023clinical}, automating eligibility assessment with LLMs \cite{wang-etal-2023-autotrial,datta2024autocriteria}, and assessing model hallucinations and reasoning capabilities in healthcare settings \cite{pal2023med,feng2023chard}. The previous SemEval NLI4CT task \cite{jullien-etal-2023-semeval} included a similar entailment task, with most submissions leveraging pre-trained language models. A small minority of approaches used ontologies and rule-based algorithms. 

Few of the language model-based approaches include preprocessing of the data prior to using it as input to the models. In our approach for the current task, we also attempt solutions based on both discriminative and generative language models, and in addition perform preprocessing of the input clinical trial data before feeding it to the models, such as summarization. While most of the best performing systems in last year's task employ some in-domain pre-training on medical data, we find that, from the models we experimented with, general LLMs perform as well as or better than medical ones. This could be explained by their larger size and instruction tuning techniques, but also because of the recent advances in general LLMs. However, we do not perform an exhaustive comparison of the two kinds of models (we use few medical LLMs in our experiments), so a definitive conclusion can not be drawn on this comparison.

The NLI4CT dataset \cite{jullien-etal-2023-nli4ct} is a unique benchmark dataset for Natural Language Inference (NLI) in the clinical domain that contains data from Clinical Trial Reports (CTRs). In contrast, the MedNLI \cite{romanov2018lessons} dataset is another benchmark dataset for NLI in the clinical domain, but it only contains clinical notes. To ensure that NLI models are robust and safe, the organizers of the NLI4CT task perform semantic-preserving and semantic-altering interventions of the hypotheses. According to \citet{jullien-etal-2023-nli4ct}, NLI models for clinical trials require not only biomedical reasoning but also numerical reasoning capabilities, as CTRs contain a large amount of quantitative information. In this regard, we conduct experiments using SOLAR 10B \cite{kim2023solar}, which was trained on question-answer pairs from the mathematical domain to enhance its mathematical capabilities.