Leveraging large language models for knowledge-free weak supervision in clinical natural language processing.

ABSTRACT: The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partial solutions to this issue, particularly using large language models (LLMs), but their performance still trails traditional supervised methods with moderate amounts of gold-standard data. In particular, inferencing with LLMs is computationally heavy. We propose an approach leveraging fine-tuning LLMs and weak supervision with virtually no domain knowledge that still achieves consistently dominant performance. Using a prompt-based approach, the LLM is used to generate weakly-labeled data for training a downstream BERT model. The weakly supervised

SUBMITTER: Hsu E 

PROVIDER: S-EPMC11893743 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature