Modeling Emotions and Ethics with Large Language Models
This addresses the need for more empathetic and principled AI interactions, though it appears incremental as it builds on existing LLM capabilities.
This paper tackles the problem of making Large Language Models (LLMs) more human-like by integrating emotions and ethics, resulting in a method that enables LLMs to generate emotionally resonant and ethically aligned content through self-evaluations and adjustments.
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.