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.

这项研究开发了用于大型语言模型（LLMs）更好地管理与情感和伦理有关的语言行为的先进方法。我们引入 DIKE，这是一个对抗性框架，可以增强 LLMs 内化和反映全球人类价值观的能力，以适应不同的文化背景，以促进用户之间的透明度和信任。该方法涉及情感的详细建模，语言行为的分类和道德保护栏的实施。我们的创新方法包括使用自监督学习技术将情感和行为进行映射，通过对抗性审查来完善这些道德保护栏，并系统地调整输出以确保道德一致性。该框架为具有道德完整性和文化敏感性的 AI 系统建立了坚实的基础，为更负责任和与环境相适应的 AI 交互铺平了道路。