Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and
Blockchain technologies have quickly gained pace as a new study niche in numerous …

There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …

Inspired by the success of the General Language Understanding Evaluation benchmark, we
introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to …

Due to the compelling improvements brought by BERT, many recent representation models
adopted the Transformer architecture as their main building block, consequently inheriting …

Motivation Information retrieval (IR) is essential in biomedical knowledge acquisition and
clinical decision support. While recent progress has shown that language model encoders …

Sentence embeddings have become an essential part of today's natural language
processing (NLP) systems, especially together advanced deep learning methods. Although …

Abstract Background Large language models (LLMs) have attracted significant interest for
automated clinical coding. However, early data show that LLMs are highly error-prone when …

There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …

Multi-task learning (MTL) has achieved remarkable success in natural language processing
applications. In this work, we study a multi-task learning model with multiple decoders on …