Summary of Exploring Large Language Model Based Intelligent Agents: Definitions, Methods, and Prospects, by Yuheng Cheng et al.
Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
by Yuheng Cheng, Ceyao Zhang, Zhengwen Zhang, Xiangrui Meng, Sirui Hong, Wenhao Li, Zihao Wang, Zekai Wang, Feng Yin, Junhua Zhao, Xiuqiang He
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an in-depth overview of Large Language Model (LLM)-based intelligent agents, which use universal natural language as an interface to exhibit robust generalization capabilities across various applications. These agents can serve as autonomous task assistants or be applied in coding, social, and economic domains. The paper covers definitions, research frameworks, foundational components, and mechanisms for deploying LLM-based agents in single-agent and multi-agent systems. It also discusses popular datasets and application scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLM-based intelligent agents have the potential to become artificial general intelligence (AGI). Researchers are working on different implementations of these agents. One type uses universal natural language as an interface, which makes them very good at understanding and doing many tasks. This paper looks at what researchers have done so far with LLM-based agents in single-agent and multi-agent systems. It explains how they work, what they can do, and how they are used. |
Keywords
» Artificial intelligence » Generalization » Large language model
