An Explainable Emotion Alignment Framework for LLM-Empowered Agent in Metaverse Service Ecosystem
This addresses the problem of integrating virtual and real-world services for Metaverse agents, but it appears incremental as it builds on existing LLM-based agent methods.
The paper tackles the challenge of aligning LLM-based agents in Metaverse services with real-world factors like data fusion and ethics, proposing an explainable emotion alignment framework and demonstrating its effectiveness in a food delivery simulation to achieve more realistic social emergence.
Metaverse service is a product of the convergence between Metaverse and service systems, designed to address service-related challenges concerning digital avatars, digital twins, and digital natives within Metaverse. With the rise of large language models (LLMs), agents now play a pivotal role in Metaverse service ecosystem, serving dual functions: as digital avatars representing users in the virtual realm and as service assistants (or NPCs) providing personalized support. However, during the modeling of Metaverse service ecosystems, existing LLM-based agents face significant challenges in bridging virtual-world services with real-world services, particularly regarding issues such as character data fusion, character knowledge association, and ethical safety concerns. This paper proposes an explainable emotion alignment framework for LLM-based agents in Metaverse Service Ecosystem. It aims to integrate factual factors into the decision-making loop of LLM-based agents, systematically demonstrating how to achieve more relational fact alignment for these agents. Finally, a simulation experiment in the Offline-to-Offline food delivery scenario is conducted to evaluate the effectiveness of this framework, obtaining more realistic social emergence.