Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in
dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inputs …

The increased use of large language models (LLMs) across a variety of real-world
applications calls for mechanisms to verify the factual accuracy of their outputs. In this work …

The goal of the NLI4CT task is to build a Natural Language Inference system for Clinical
Trial Reports that will be used for evidence interpretation and retrieval. Large Language …

In this work, we address the challenge of identifying the inference relation between a plain
language statement and Clinical Trial Reports (CTRs) by using a T5-large model …

To improve the ability of language models to handle Natural Language Processing (NLP)
tasks and intermediate step of pre-training has recently beenintroduced. In this setup, one …

With the increasing number of clinical trial reports generated every day, it is becoming hard
to keep up with novel discoveries that inform evidence-based healthcare recommendations …

In this paper, we present our system for the textual entailment identification task as a subtask
of the SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial …

Numbers are frequently utilized in both our daily narratives and professional documents,
such as clinical notes, scientific papers, financial documents, and legal court orders. The …

Background Early-phase clinical trials (EPCT) represent an important part of innovations in
medical oncology and a valuable therapeutic option for patients with metastatic cancers …

Inferring over and extracting information from Large Language Models (LLMs) trained on a
large corpus of scientific literature can potentially drive a new era in biomedical research …