Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models

TL;DR

This paper presents DIKE, an adversarial framework that enhances Large Language Models' ability to reflect human values, emotions, and ethics, ensuring culturally sensitive and trustworthy AI interactions.

Contribution

Introduction of DIKE, a novel adversarial framework that improves ethical and emotional alignment in LLMs through self-supervised learning and adversarial refinement.

Findings

Enhanced ethical compliance in LLM outputs

Improved cultural sensitivity and trustworthiness

Robust modeling of emotions and behaviors

Abstract

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.