Alignment techniques have become central to ensuring that Large Language Models (LLMs) generate outputs consistent with human values. However, existing alignment paradigms often model an averaged or monolithic preference, failing to account for the diversity of perspectives across cultures, demographics, and communities. This limitation is particularly critical in health-related scenarios, where plurality is essential due to the influence of culture, religion, personal values, and conflicting opinions. Despite progress in pluralistic alignment, no prior work has focused on health, likely due to the unavailability of publicly available datasets. To address this gap, we introduce VITAL, a new benchmark dataset comprising 13.1K value-laden situations and 5.4K multiple-choice questions focused on health, designed to assess and benchmark pluralistic alignment methodologies. Through extensive evaluation of eight LLMs of varying sizes, we demonstrate that existing pluralistic alignment techniques fall short in effectively accommodating diverse healthcare beliefs, underscoring the need for tailored AI alignment in specific domains. This work highlights the limitations of current approaches and lays the groundwork for developing health-specific alignment solutions.

本研究针对现有对齐技术未能充分考虑医疗领域中多样化观点这一问题，提出了一个新数据集VITAL，包含13100个价值导向的情境和5400个多选题，专注于健康领域的多元对齐评估。研究发现，现有的多元对齐技术在满足不同医疗信仰方面的有效性不足，突显了在特定领域发展量身定制的人工智能对齐的必要性。

VITAL: 一个用于医疗领域多元对齐基准的新数据集