The Growth Behind LLM-based Autonomous Agents

Let’s dive into a survey that has been curated on the growth of LLM-based autonomous agents.

The Growth Behind LLM-based Autonomous Agents
Image by Editor | DALL-E 3


A lot has happened in the year 2023. We’ve seen the growth and emergence of Large Language Models (LLMs) and their particular use as fundamental controllers for autonomous agents. We’ve seen it for ourselves, many people have adopted these autonomous agents, and integrated them into organizations and more companies are interested in LLMs.

And yes, they have been successful. 

But don’t you want to know more? Of course, you do. 


Survey on LLM-based Autonomous Agents


Researchers from Gaoling School of Artificial Intelligence, Renmin University of China have come together to perform a comprehensive survey on LLM-based autonomous agents, in which they deliver a systematic review of the field of LLM-based autonomous agents from a holistic perspective.

The researchers delve into the construction of LLM-based autonomous agents aswell as a  comprehensive overview of the diverse applications in a variety of fields, such as social science, natural science, and engineering. 

So let’s get into it.

Below is an image of the growth trend in the field of LLM-based autonomous agents, through the number of published papers from January 2021 to August 2023. 

As you can see, in the space of 2 years, LLMs have achieved notable successes, showing the wider public that AI applications have the potential to attain human-like intelligence. Comprehensive training datasets and a substantial number of model parameters work hand in hand in order to attain this. 

So it seems like there is a lot of funding and research going into this field, therefore it is imperative to provide a systematic summary of the rapidly developing field to comprehensively understand the intricacies behind it and the benefits it will bring to inspire future research.

This is what this research team from the Gaoling School of Artificial Intelligence are doing. 

The Growth Behind LLM-based Autonomous Agents
Image by LLM-Agent-Survey


Architecture Design of LLM-based Autonomous Agent


The whole aim behind LLM-based autonomous agents is that they have the ability to perform diverse tasks as if they have human-like capabilities. For this to be achievable, you need to look further into the architecture design of LLM-based autonomous agents:

  1. Which architecture should be designed to better use LLMs 
  2. How to enable the agent to acquire capabilities for accomplishing specific tasks

As part of the systematic review, the researchers understood that LLMs need to fulfill specific roles and autonomously learn from the environment in order to evolve themselves like humans. This is where design rational agent architectures come into play.

The researchers have proposed a unified framework to summarize the number of developed modules to enhance LLMs:

  • Profile - identify the role of the agent
  • Memory - place the agent into a dynamic environment and enable it to recall past behaviors
  • Planning - place the agent into a dynamic environment and plan future actions.
  • Action - translating the agent’s decisions into specific outputs

The profiling module has a direct impact on the memory and planning modules, which all together these three modules influence the action module. 

The Growth Behind LLM-based Autonomous Agents
Image by LLM-Agent-Survey


To delve into each module in depth, have a read of the paper: A Survey on Large Language Model-based Autonomous Agents.

In this paper, you can have a deeper look into the applications of LLM-based autonomous agents and proposed evaluation strategies in three distinct areas: social science, natural science, and engineering. LLM-based autonomous agents have shown significant potential to influence multiple domains, therefore, understanding how these applications are evaluated and the strategies used is important.

The Growth Behind LLM-based Autonomous Agents
Image by Survey LLM-based Autonomous Agents


As part of the research process, they also have an interactive table that contains more comprehensive papers related to LLM-based Agents.


Wrapping it up


As we can see, more and more people are peeling the skin back when it comes to LLMs. More people want to know what it’s really about, the architecture, the evaluation strategies and how it will impact our future. Is this to help build more trust around LLMs and AI applications in general or are we going to learn the truth about them?

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.