Large language models (LLMs) present a valuable technology for various applications in healthcare, but their tendency to hallucinate introduces unacceptable uncertainty in critical decision-making situations. Human-AI collaboration (HAIC) can mitigate this uncertainty by combining human and AI strengths for better outcomes. This paper presents a novel guided deferral system that provides intelligent guidance when AI defers cases to human decision-makers. We leverage LLMs' verbalisation capabilities and internal states to create this system, demonstrating that fine-tuning small-scale LLMs with data from large-scale LLMs greatly enhances performance while maintaining computational efficiency and data privacy. A pilot study showcases the effectiveness of our proposed deferral system.

翻译：大型语言模型（LLMs）为医疗保健领域的多种应用提供了有价值的技术，但其产生幻觉的倾向在关键决策场景中引入了不可接受的不确定性。人机协作（HAIC）可以通过结合人类与人工智能的优势来缓解这种不确定性，从而获得更好的结果。本文提出了一种新颖的引导式转交系统，该系统在人工智能将案例转交给人类决策者时提供智能引导。我们利用LLMs的言语化能力及其内部状态来构建此系统，并证明使用来自大规模LLMs的数据对小型LLMs进行微调，可以在保持计算效率与数据隐私的同时显著提升性能。一项初步研究展示了我们所提出的转交系统的有效性。