Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

Pre-trained language models have attracted increasing attention in the biomedical domain,
inspired by their great success in the general natural language domain. Among the two main …

Information overload is a major obstacle to scientific progress. The explosive growth in
scientific literature and data has made it ever harder to discover useful insights in a large …

Language model (LM) pretraining can learn various knowledge from text corpora, helping
downstream tasks. However, existing methods such as BERT model a single document, and …

There are enormous enthusiasm and concerns in applying large language models (LLMs) to
healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT …

Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …

Pretraining large neural language models, such as BERT, has led to impressive gains on
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …

Recent advancements in large language models (LLMs) have led to the development of
highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional …

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