Local Large Language Models for Complex Structured Tasks.

This paper introduces an approach that combines the language reasoning capabilities of large language models (LLMs) with the benefits of local training to tackle complex language tasks. The authors demonstrate their approach by extracting structured condition codes from pathology reports. The proposed approach utilizes local, fine-tuned LLMs to respond to specific generative instructions and provide structured outputs.