Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …

Recent advancements in large language models (LLMs) have transformed the field of
question answering (QA). However, evaluating LLMs in the medical field is challenging due …

Large language models (LLMs) have shown promise for generative and knowledge-
intensive tasks including question-answering (QA) tasks. However, the practical deployment …

Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

Transformer-based pretrained language models (PLMs) have started a new era in modern
natural language processing (NLP). These models combine the power of transformers …

Large Language Models (LLMs) provide a possibility to make a great breakthrough in
medicine. The establishment of a standardized medical benchmark becomes a fundamental …

Automatic Question Answering (QA) has been successfully applied in various domains such
as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables …

Question answering (QA) systems provide a way of querying the information available in
various formats including, but not limited to, unstructured and structured data in natural …

Machine reading comprehension (MRC), which requires a machine to answer questions
based on a given context, has attracted increasing attention with the incorporation of various …