Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues
It addresses the issue of inauthentic dialogues in multi-agent simulations for researchers and developers, representing an incremental improvement over existing methods like Generative Agents.
This paper tackled the problem of low-quality multi-agent dialogues in LLM-powered simulations, which suffer from repetition, inconsistency, and hallucination, by proposing a Screening, Diagnosis, and Regeneration (SDR) framework that enhances diversity, consistency, and factualness.
This paper investigates the quality of multi-agent dialogues in simulations powered by Large Language Models (LLMs). 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. By incorporating our SDR framework to Generative Agents (Park et al., 2023), we enhance the diversity, consistency, and factualness of the generated dialogues. This work presents a pioneering approach to enhancing dialogue quality in multi-agent simulations, establishing a new standard for future research in the field.