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, domain-specific tasks. Specifically, the authors
demonstrate their approach by extracting structured condition codes from
pathology reports. The proposed approach utilizes local LLMs, which can be
fine-tuned to respond to specific generative instructions and provide
structured outputs. The authors collected a dataset of over 150k uncurated
surgical pathology reports, containing gross descriptions, final diagnoses, and
condition codes. They trained different model architectures, including LLaMA,
BERT and LongFormer and evaluated their performance. The results show that the
LLaMA-based models significantly outperform BERT-style models across all
evaluated metrics, even with extremely reduced precision. The LLaMA models
performed especially well with large datasets, demonstrating their ability to
handle complex, multi-label tasks. Overall, this work presents an effective
approach for utilizing LLMs to perform domain-specific tasks using accessible
hardware, with potential applications in the medical domain, where complex data
extraction and classification are required.

本论文介绍了一种将大型语言模型（LLMs）的语言推理能力与本地训练的优势相结合的方法，以解决复杂的领域特定任务。通过从病理报告中提取结构化病情编码，作者演示了他们的方法。研究结果表明，基于 LLaMA 的模型在所有评估指标上明显优于 BERT 型模型，尤其在大型数据集上表现出色，为处理复杂的多标签任务提供了可能性。这项工作提出了一种有效的方法，利用 LLMs 在易于获得的硬件上执行领域特定任务，在需要复杂数据提取和分类的医学领域具有潜在应用。