Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models
Abstract: This research develops advanced methodologies for Large Language Models (LLMs) to better manage linguistic behaviors related to emotions and ethics. We introduce DIKE, a framework that enhances the LLMs' ability to internalize and reflect universal 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.
Paper Type: Long
Research Area: Ethics, Bias, and Fairness
Research Area Keywords: AI safety, Linguistic behavior
Contribution Types: Model analysis & interpretability, Position papers
Languages Studied: English
Submission Number: 142
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