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 …

The ability of large language models (LLMs) to follow natural language instructions with
human-level fluency suggests many opportunities in healthcare to reduce administrative …

A patient portal allows discharged patients to access their personalized discharge
instructions in electronic health records (EHRs). However, many patients have difficulty …

Reading is integral to everyday life, and yet learning to read is a struggle for many young
learners. During lessons, teachers can use comprehension questions to increase …

We propose task-adaptive tokenization as a way to adapt the generation pipeline to the
specifics of a downstream task and enhance long-form generation in mental health. Inspired …

Existing question answering (QA) datasets derived from electronic health records (EHR) are
artificially generated and consequently fail to capture realistic physician information needs …

Question generation (QG) aims to generate natural questions from relevant input. Existing
state-of-the-art QG approaches primarily leverage pre-trained language models (PLMs) to …

Question generation (QG), the task of generating questions from text or other forms of data, a
significant and challenging subject, has recently attracted more attention in natural language …

With the outbreak of COVID-19 that has prompted an increased focus on self-care, more and
more people hope to obtain disease knowledge from the Internet. In response to this …

This study introduces EHRNoteQA, a novel patient-specific question answering benchmark
tailored for evaluating Large Language Models (LLMs) in clinical environments. Based on …