Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction

• URL: http://arxiv.org/abs/2310.06239v1
• Date: Tue, 10 Oct 2023 01:27:08 GMT
• Title: Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
• Authors: Cheng Peng, Xi Yang, Kaleb E Smith, Zehao Yu, Aokun Chen, Jiang Bian, Yonghui Wu
• Abstract summary: We develop soft prompt-based learning algorithms for large language models (LLMs) We compare 4 training strategies including fine-tuning without prompts, hard-prompt with unfrozen LLMs, soft-prompt with frozen LLMs, and soft-prompt with frozen LLMs. We evaluate the transfer learning ability of the prompt-based learning algorithms in a cross-institution setting.
• Score: 26.504643007899592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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