  This paper investigates the quality of multi-agent dialogues in simulations
powered by Large Language Models (LLMs), focusing on a case study from Park et
al. (2023), where 25 agents engage in day-long simulations of life, showcasing
complex behaviors and interactions. Analyzing dialogues and memory over
multiple sessions revealed significant issues such as repetition,
inconsistency, and hallucination, exacerbated by the propagation of erroneous
information. To combat these challenges, we propose a novel Screening,
Diagnosis, and Regeneration (SDR) framework that detects and corrects utterance
errors through a comprehensive process involving immediate issue
identification, evidence gathering from past dialogues, and LLM analysis for
utterance revision. The effectiveness of the SDR framework is validated through
GPT-4 assessments and human evaluations, demonstrating marked improvements in
dialogue consistency, diversity, and the reduction of false information. This
work presents a pioneering approach to enhancing dialogue quality in
multi-agent simulations, establishing a new standard for future research in the
field.
