Exploring and steering the moral compass of Large Language Models

The paper explores the ethical implications of large language models (LLMs) in AI technology, focusing on their moral reasoning capabilities and the need for responsible development. It compares eight LLMs' responses to ethical dilemmas, revealing a Western-centric bias and the importance of diverse perspectives in training data. The study highlights the inconsistency between proprietary and open-source models, with proprietary ones leaning towards utilitarianism and open ones favoring deontology. The authors propose a method, SARA, to steer model behavior without retraining, enhancing transparency and ethical consistency. SARA's effectiveness varies depending on the model and layer intervention. The research also examines cultural influences on moral profiles of AI, suggesting the need for ethical considerations in AI development to minimize harm and promote fairness. The paper concludes by emphasizing the need for a comprehensive approach to AI safety, including the evaluation of models' performance on moral foundations and the potential for activation steering to guide ethical decision-making.