Large Language Models (LLMs) have demonstrated remarkable adaptability,
showcasing their capacity to excel in tasks for which they were not explicitly
trained. However, despite their impressive natural language processing (NLP)
capabilities, effective alignment of LLMs remains a crucial challenge when
deploying them for specific clinical applications. The ability to generate
responses with factually accurate content and to engage in non-trivial
reasoning steps are crucial for the LLMs to be eligible for applications in
clinical medicine. Employing a combination of techniques including
instruction-tuning and in-prompt strategies like few-shot and chain of thought
prompting has significantly enhanced the performance of LLMs. Our proposed
alignment strategy for medical question-answering, known as
'expand-guess-refine', offers a parameter and data-efficient solution. A
preliminary analysis of this method demonstrated outstanding performance,
achieving a score of 70.63% on a subset of questions sourced from the USMLE
dataset.

大型语言模型在临床应用中的关键性挑战是对其进行有效的对齐，以实现准确生成具备事实内容和非平凡推理能力的响应。本研究提出了一种名为 “扩展 - 猜测 - 精化” 的医学问题回答的对齐策略，该策略通过采用指令调整和少样本以及连续思考等方法显著提高了大型语言模型的性能。初步分析表明，该方法在从 USMLE 数据集中选取的问题子集上达到了 70.63% 的优异表现。