Summary of Computational Experiments Meet Large Language Model Based Agents: a Survey and Perspective, by Qun Ma et al.
Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective
by Qun Ma, Xiao Xue, Deyu Zhou, Xiangning Yu, Donghua Liu, Xuwen Zhang, Zihan Zhao, Yifan Shen, Peilin Ji, Juanjuan Li, Gang Wang, Wanpeng Ma
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the integration of Large Language Models (LLMs) with Agent-based Modeling (ABM) to create more realistic artificial societies. LLMs enable agents to possess complex reasoning and autonomous learning capabilities, enhancing their anthropomorphism. However, this fusion also raises concerns about explainability. The authors discuss the historical development of agent structures, their evolution into artificial societies, and the advantages they offer each other in terms of computational experiments. They then address challenges and future trends in this research domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Computational experiments are a way to study complex systems by using algorithms. Agent-based Modeling (ABM) is one type of experiment that helps us understand how people behave. However, making ABM more realistic is tricky because humans can be unpredictable. To make things better, scientists have proposed combining ABM with Large Language Models (LLMs). LLMs are like super-smart computers that can learn and reason. This combination could lead to more realistic artificial societies. The paper looks at how this fusion works and what challenges we might face. |
