Developing Healthcare Language Model Embedding Spaces
Niall Taylor Dan Schofield Andrey Kormilitzin Dan W Joyce Alejo Nevado-Holgado
Pre-trained Large Language Models (LLMs) often struggle on out-of-domain
datasets like healthcare focused text. We explore specialized pre-training to
adapt smaller LLMs to different healthcare datasets. Three methods are
assessed: traditional masked language modeling, Deep Contrastive Learning for
Unsupervised Textual Representations (DeCLUTR), and a novel pre-training
objective utilizing metadata categories from the healthcare settings. These
schemes are evaluated on downstream document classification tasks for each
dataset, with additional analysis of the resultant embedding spaces.
Contrastively trained models outperform other approaches on the classification
tasks, delivering strong performance from limited labeled data and with fewer
model parameter updates required. While metadata-based pre-training does not
further improve classifications across the datasets, it yields interesting
embedding cluster separability. All domain adapted LLMs outperform their
publicly available general base LLM, validating the importance of
domain-specialization. This research illustrates efficient approaches to
instill healthcare competency in compact LLMs even under tight computational
budgets, an essential capability for responsible and sustainable deployment in
local healthcare settings. We provide pre-training guidelines for specialized
healthcare LLMs, motivate continued inquiry into contrastive objectives, and
demonstrates adaptation techniques to align small LLMs with privacy-sensitive
medical tasks.