Towards citizen-centric multiagent systems based on large language models

Abstract

The rapid evolution of Large Language Models (LLMs), exemplified by GPT-4, has ushered in a transformative era in artificial intelligence (AI). This paper introduces the concept of Citizen-Centric Multiagent Systems based on Large Language Models (C-LLMAS) and advocates for LLMs as pivotal technology for this vision. We present a comprehensive framework that places citizens at the core of multiagent systems, ensuring user-friendly interactions, bidirectional feedback, and dynamic user participation. Key contributions of this paper include proposing a framework for C-LLMAS that integrates LLMs to enhance citizen engagement, feedback loops, and dynamic involvement; identifying and discussing critical research challenges such as personalized citizen modeling, safeguarding citizen interests, enhancing security, and improving explainability; and highlighting collaborative research opportunities that demonstrate the potential of LLMs in various domains, including transportation, healthcare, and education. By addressing these challenges and exploring these opportunities, this paper aims to integrate LLMs into C-LLMAS responsibly, ultimately enhancing citizens’ social good and trust in AI systems.

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Identifiers

ORCID for Zhaoxing Li: orcid.org/0000-0003-3560-3461

ORCID for Sebastian Stein: orcid.org/0000-0003-2858-8857

ORCID for Vahid Yazdanpanah: orcid.org/0000-0002-4468-6193

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