Pluralistic Alignment for Healthcare: A Role-Driven Framework
This addresses the need for adaptable alignment in sensitive domains like healthcare, where existing methods fall short, though it appears incremental in applying pluralistic concepts specifically to health.
The paper tackles the problem of aligning large language models in healthcare to reflect diverse values and perspectives, proposing EthosAgents as a lightweight, generalizable approach that advances pluralistic alignment across seven models.
As large language models are increasingly deployed in sensitive domains such as healthcare, ensuring their outputs reflect the diverse values and perspectives held across populations is critical. However, existing alignment approaches, including pluralistic paradigms like Modular Pluralism, often fall short in the health domain, where personal, cultural, and situational factors shape pluralism. Motivated by the aforementioned healthcare challenges, we propose a first lightweight, generalizable, pluralistic alignment approach, EthosAgents, designed to simulate diverse perspectives and values. We empirically show that it advances the pluralistic alignment for all three modes across seven varying-sized open and closed models. Our findings reveal that health-related pluralism demands adaptable and normatively aware approaches, offering insights into how these models can better respect diversity in other high-stakes domains.