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 …

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

Despite the widespread success of self-supervised learning via masked language models
(MLM), accurately capturing fine-grained semantic relationships in the biomedical domain …

The utilization of large language models (LLMs) in the Healthcare domain has generated
both excitement and concern due to their ability to effectively respond to freetext queries with …

Pretrained language models have served as important backbones for natural language
processing. Recently, in-domain pretraining has been shown to benefit various domain …

Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …

Pretrained Masked Language Models (MLMs) have revolutionised NLP in recent years.
However, previous work has indicated that off-the-shelf MLMs are not effective as universal …

Abstract Recently, Large Language Models (LLMs) have demonstrated impressive
capability to solve a wide range of tasks. However, despite their success across various …

Unsupervised pretraining is an integral part of many natural language processing systems,
and transfer learning with language models has achieved remarkable results in downstream …

Abstract Biomedical Entity Linking (BEL) is the task of mapping of spans of text within
biomedical documents to normalized, unique identifiers within an ontology. This is an …