LLM Instruction-Example Adaptive Prompting (LEAP) Framework for Clinical Relation Extraction.

Objective: To investigate the demonstration in Large Language Models (LLMs) for clinical relation extraction. We focus on examining two types of adaptive demonstration: instruction adaptive prompting, and example adaptive prompting to understand their impacts and effectiveness. Materials and Methods: The study unfolds in two stages. Initially, we explored a range of demonstration components vital to LLMs' clinical data extraction, such as task descriptions and examples, and tested their combinations. Subsequently, we introduced the Instruction-Example Adaptive Prompting (LEAP) Framework, a system that integrates two types of adaptive prompts: one preceding instruction and another before examples. This framework is designed to systematically explore both adaptive task description and adaptive examples within the demonstration. We evaluated LEAP framework's performance on the DDI and BC5CDR chemical interaction datasets, applying it across LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B. Results: The study revealed that Conclusion: The LEAP framework presents a promising avenue for refining LLM training strategies, steering away from extensive finetuning towards more contextually rich and dynamic prompting methodologies.