Objective To develop soft prompt-based learning algorithms for large language
models (LLMs), examine the shape of prompts, prompt-tuning using
frozen/unfrozen LLMs, transfer learning, and few-shot learning abilities.
Methods We developed a soft prompt-based LLM model and compared 4 training
strategies including (1) fine-tuning without prompts; (2) hard-prompt with
unfrozen LLMs; (3) soft-prompt with unfrozen LLMs; and (4) soft-prompt with
frozen LLMs. We evaluated 7 pretrained LLMs using the 4 training strategies for
clinical concept and relation extraction on two benchmark datasets. We
evaluated the transfer learning ability of the prompt-based learning algorithms
in a cross-institution setting. We also assessed the few-shot learning ability.
Results and Conclusion When LLMs are unfrozen, GatorTron-3.9B with soft
prompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept
extraction, outperforming the traditional fine-tuning and hard prompt-based
models by 0.6~3.1% and 1.2~2.9%, respectively; GatorTron-345M with soft
prompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end
relation extraction, outperforming the other two models by 0.2~2% and
0.6~11.7%, respectively. When LLMs are frozen, small (i.e., 345 million
parameters) LLMs have a big gap to be competitive with unfrozen models; scaling
LLMs up to billions of parameters makes frozen LLMs competitive with unfrozen
LLMs. For cross-institute evaluation, soft prompting with a frozen
GatorTron-8.9B model achieved the best performance. This study demonstrates
that (1) machines can learn soft prompts better than humans, (2) frozen LLMs
have better few-shot learning ability and transfer learning ability to
facilitate muti-institution applications, and (3) frozen LLMs require large
models.

开发用于大型语言模型的软提示学习算法，研究词干的形状、使用冻结 / 非冻结语言模型进行文字调优、迁移学习和少样本学习的能力。