构建医疗领域多元价值观对齐数据集，评测大模型在不同文化信念下的响应差异。
VITAL: A New Dataset for Benchmarking Pluralistic Alignment in Healthcare
- 设计包含13.1K个价值敏感情境的医疗对齐数据集
- 8个大模型在该数据集上均未能有效捕捉多元医疗信念
- 为医疗AI对齐提供可复现的评估基准，适合健康AI研究者
对齐技术已成为确保大语言模型输出符合人类价值观的核心手段。然而，现有对齐范式通常建模平均或单一偏好，未能反映文化、人口统计学及社群间的观点多样性。这一局限在医疗场景中尤为关键，因文化、宗教、个人价值观和争议性意见的影响显著。尽管在多元对齐方面已有进展，但此前尚无针对医疗领域的公开数据集，制约了相关研究。为此，我们提出VITAL——一个包含13.1K个价值敏感医疗情境与5.4K道多选题的新基准数据集，用于评估和比较多元对齐方法。通过对8个不同规模的大语言模型进行广泛评估，我们发现现有多元对齐技术在处理多样医疗信念时表现不佳，凸显了特定领域（如医疗）定制化对齐的必要性。本工作揭示了当前方法的局限，为开发面向医疗场景的对齐解决方案奠定了基础。
原文摘要 · Abstract (English)
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.
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