The Art of Midwifery in LLMs: Optimizing Role Personas for Large Language Models as Moral Assistants
This work addresses the challenge of enhancing moral growth in users through AI assistance, offering a novel approach beyond traditional alignment paradigms, though it is incremental in refining existing concepts.
The study tackled the problem of LLMs in moral decision-making by proposing they act as 'moral assistants' with distinct personas, finding that optimal persona performance varied by scenario, such as the Guardian Angel excelling in bioethical crises and the Socratic persona in existential dilemmas.
With the development of Large Language Models (LLMs) in consulting, their role in moral decision-making has become prominent. However, existing research predominantly consider AI as an independent "moral agent" adhering to the "Human-AI Alignment" paradigm. In this study, we propose that AI should serve as a "moral assistant", facilitating users' moral growth through the "Art of Midwifery" rather than substituting human judgment. We endow LLMs with distinct persona archetypes and conducted dialogues across six moral scenarios. Findings reveal that while the virtue exemplar excelled overall, optimal performance was context-dependent: the Guardian Angel excelled in bioethical crises for emotional support, whereas the Socratic persona better elicited reflection in existential dilemmas. We introduce "Constructive Divergence", arguing that AI should offer alternative perspectives at critical moment rather than blindly accommodate users, transcending traditional alignment paradigms.