  Objective To solve major clinical natural language processing (NLP) tasks
using a unified text-to-text learning architecture based on a generative large
language model (LLM) via prompt tuning. Methods We formulated 7 key clinical
NLP tasks as text-to-text learning and solved them using one unified generative
clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with
up to 20 billion parameters. We adopted soft prompts (i.e., trainable vectors)
with frozen LLM, where the LLM parameters were not updated (i.e., frozen) and
only the vectors of soft prompts were updated, known as prompt tuning. We added
additional soft prompts as a prefix to the input layer, which were optimized
during the prompt tuning. We evaluated the proposed method using 7 clinical NLP
tasks and compared them with previous task-specific solutions based on
Transformer models. Results and Conclusion The proposed approach achieved
state-of-the-art performance for 5 out of 7 major clinical NLP tasks using one
unified generative LLM. Our approach outperformed previous task-specific
transformer models by ~3% for concept extraction and 7% for relation extraction
applied to social determinants of health, 3.4% for clinical concept
normalization, 3.4~10% for clinical abbreviation disambiguation, and 5.5~9% for
natural language inference. Our approach also outperformed a previously
developed prompt-based machine reading comprehension (MRC) model,
GatorTron-MRC, for clinical concept and relation extraction. The proposed
approach can deliver the ``one model for all`` promise from training to
deployment using a unified generative LLM.
