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

2024/05/11 by Edward Y. Chang, Chang, Edward Y.
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #I.2.7

paper · pdf · doi:10.48550/arxiv.2405.07076

openalex publication_date 2024/05/11 · openalex created_date 2024/05/16 · openalex updated_date 2026/07/28

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.

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