In difficult decision-making scenarios, it is common to have conflicting
opinions among expert human decision-makers as there may not be a single right
answer. Such decisions may be guided by different attributes that can be used
to characterize an individual's decision. We introduce a novel dataset for
medical triage decision-making, labeled with a set of decision-maker attributes
(DMAs). This dataset consists of 62 scenarios, covering six different DMAs,
including ethical principles such as fairness and moral desert. We present a
novel software framework for human-aligned decision-making by utilizing these
DMAs, paving the way for trustworthy AI with better guardrails. Specifically,
we demonstrate how large language models (LLMs) can serve as ethical
decision-makers, and how their decisions can be aligned to different DMAs using
zero-shot prompting. Our experiments focus on different open-source models with
varying sizes and training techniques, such as Falcon, Mistral, and Llama 2.
Finally, we also introduce a new form of weighted self-consistency that
improves the overall quantified performance. Our results provide new research
directions in the use of LLMs as alignable decision-makers. The dataset and
open-source software are publicly available at:
this https URL

医学分级决策中的决策者属性的使用和大型语言模型作为道德决策者的应用研究。