Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach

Researchers developed a supervised fine-tuning approach to align large language model agents with specific economic preferences, addressing systematic deviations from rational behavior in strategic environments. The study demonstrates how LLM agents can be trained to follow either self-interested or morally-guided strategies, producing distinct outcomes in economic games and pricing scenarios.