Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View
CoRR(2023)
Abstract
As Natural Language Processing (NLP) systems are increasingly employed in
intricate social environments, a pressing query emerges: Can these NLP systems
mirror human-esque collaborative intelligence, in a multi-agent society
consisting of multiple large language models (LLMs)? This paper probes the
collaboration mechanisms among contemporary NLP systems by melding practical
experiments with theoretical insights. We fabricate four unique `societies'
comprised of LLM agents, where each agent is characterized by a specific
`trait' (easy-going or overconfident) and engages in collaboration with a
distinct `thinking pattern' (debate or reflection). Through evaluating these
multi-agent societies on three benchmark datasets, we discern that certain
collaborative strategies not only outshine previous top-tier approaches, but
also optimize efficiency (using fewer API tokens). Moreover, our results
further illustrate that LLM agents manifest human-like social behaviors, such
as conformity and consensus reaching, mirroring foundational social psychology
theories. In conclusion, we integrate insights from social psychology to
contextualize the collaboration of LLM agents, inspiring further investigations
into the collaboration mechanism for LLMs. We commit to sharing our code and
datasets[.], hoping to
catalyze further research in this promising avenue.
MoreTranslated text
Key words
collaboration mechanisms,llm agents,social psychology
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined