  This research develops advanced methodologies for Large Language Models
(LLMs) to better manage linguistic behaviors related to emotions and ethics. We
introduce DIKE, an adversarial framework that enhances the LLMs' ability to
internalize and reflect global human values, adapting to varied cultural
contexts to promote transparency and trust among users. The methodology
involves detailed modeling of emotions, classification of linguistic behaviors,
and implementation of ethical guardrails. Our innovative approaches include
mapping emotions and behaviors using self-supervised learning techniques,
refining these guardrails through adversarial reviews, and systematically
adjusting outputs to ensure ethical alignment. This framework establishes a
robust foundation for AI systems to operate with ethical integrity and cultural
sensitivity, paving the way for more responsible and context-aware AI
interactions.
