整合情感与语言模型以实现大语言模型的伦理合规
Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models
AI总结：
本研究提出对抗性框架DIKE，通过情感建模、语言行为分类、伦理护栏构建及对抗优化等方法，提升大语言模型的伦理合规性与文化适配性，推动负责任的AI交互。
摘要：
本研究为大语言模型（LLMs）开发了先进方法，以更好地管理与情感和伦理相关的语言行为。我们提出DIKE，这是一个对抗性框架，可增强LLMs内化并反映人类普世价值的能力，适配不同文化背景，以提升用户透明度与信任度。该方法涉及情感的精细化建模、语言行为的分类以及伦理护栏的落地。我们的创新方法包括利用自监督学习技术映射情感与行为，通过对抗性审查优化这些护栏，并系统性调整输出以确保伦理对齐。该框架为AI系统以伦理完整性和文化敏感性运行奠定了坚实基础，为更负责任、具备情境感知能力的AI交互铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
