VITAL: A New Dataset for Benchmarking Pluralistic Alignment in Healthcare

TL;DR

This paper introduces VITAL, a comprehensive dataset for benchmarking pluralistic alignment in healthcare, highlighting the challenges of aligning AI models with diverse health-related human values and beliefs.

Contribution

The paper presents VITAL, the first publicly available dataset for pluralistic alignment in health, enabling evaluation and development of models respecting diverse healthcare perspectives.

Findings

Existing alignment techniques are insufficient for diverse healthcare beliefs.

LLMs struggle to accommodate cultural and personal health values.

VITAL enables targeted improvements in health-specific AI alignment.

Abstract

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.