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.

翻译：本研究开发了面向大语言模型（LLMs）的先进方法论，以更有效地管理与情感和伦理相关的语言行为。我们提出DIKE对抗性框架，增强LLMs内化并反映全球人类价值观的能力，使其适应多元文化语境，从而提升用户间的透明度与信任度。该方法涵盖情感精细建模、语言行为分类及伦理护栏的实施。创新方法包括：利用自监督学习技术映射情感与行为、通过对抗性评审优化伦理护栏，以及系统化调整输出以保障伦理一致性。该框架为人工智能系统在伦理完整性与文化敏感性方面的运行奠定了坚实基础，为推动更负责任且情境感知的AI交互开辟了新路径。