RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplements.

Objective: To develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records.

Methods: We focused on 4 core DS information extraction tasks: named entity recognition (2 949 clinical sentences), relation extraction (4 892 sentences), triple extraction (2 949 sentences), and usage classification (2 460 sentences). To address these tasks, we introduced the retrieval-augmented multi-task information extraction (RAMIE) framework, which incorporates: (1) instruction fine-tuning with task-specific prompts; (2) multi-task training of LLMs to enhance storage efficiency and reduce training costs; and (3) retrieval-augmented generation, which retrieves similar examples from the training set to improve task performance.