Modeling Emotions and Ethics with Large Language Models

TL;DR

This paper presents methods for embedding human-like emotions and ethical considerations into Large Language Models, enabling more empathetic and ethically aligned AI interactions through novel modeling and self-supervised learning techniques.

Contribution

Introduces a new framework for modeling emotions and ethics in LLMs using opposing emotion pairs and a self-supervised learning algorithm guided by human feedback.

Findings

LLMs can express a spectrum of fundamental human emotions.

The proposed SSHF algorithm improves ethical content generation.

Models demonstrate enhanced empathetic and ethical decision-making capabilities.

Abstract

This paper explores the integration of human-like emotions and ethical considerations into Large Language Models (LLMs). We first model eight fundamental human emotions, presented as opposing pairs, and employ collaborative LLMs to reinterpret and express these emotions across a spectrum of intensity. Our focus extends to embedding a latent ethical dimension within LLMs, guided by a novel self-supervised learning algorithm with human feedback (SSHF). This approach enables LLMs to perform self-evaluations and adjustments concerning ethical guidelines, enhancing their capability to generate content that is not only emotionally resonant but also ethically aligned. The methodologies and case studies presented herein illustrate the potential of LLMs to transcend mere text and image generation, venturing into the realms of empathetic interaction and principled decision-making, thereby setting a new precedent in the development of emotionally aware and ethically conscious AI systems.