paitesanshi / llm-agent-survey Goto Github PK

web url：https://appagent-official.github.io/
Code：https://github.com/mnotgod96/AppAgent
paper：https://arxiv.org/abs/2312.13771

Hi,

Thanks for compiling this thorough and excellent review (article and website)!

Here's just a pointer to an early paper which set up a simulation agent and might be worth mentioning:

• "Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics" https://www.jasss.org/25/1/2.html

Best,
Gregor

Hi, I was wondering if you have an updated list of LLM papers? You have a really nice chart that goes until August 2023 but it would be great to have an updated version or at least a list of all LLM papers by date. Do you have this?

Hello, will the unified agent framework mentioned in the article be open source?

就是这个表格
https://abyssinian-molybdenum-f76.notion.site/237e9f7515d543c0922c74f4c3012a77?v=0a309e53d6454afcbe7a5a7e169be0f9

This aspect of our world is moving so fast its very difficult for us to keep track. Resources like this one you have put together are incredibly helpful

Hello,

Thanks for your comprehensive and inspiring paper list! I'd like to share our recent work titled "Empowering Large Language Model Agents through Action Learning," which may be of interest to the paper list readers. The paper may be added to the Planning Section.

Paper: https://arxiv.org/abs/2402.15809
Code: https://github.com/zhao-ht/LearnAct
This work proposes the LearnAct framework, which employs an iterative learning approach to dynamically create and refine learnable actions (skills). By evaluating and amending actions in response to errors observed during unsuccessful training episodes, LearnAct systematically increases the efficiency and adaptability of actions undertaken by Large Language Model (LLM) agents.
The experiment conducted within the contexts of Robotic Planning and Alfworld environments demonstrated that LearnAct can significantly enhance agent performance on given tasks.

I hope this contributes to the great paper list!

The URLs for paper "API-Bank" are missing.

Currently they point to https://github.com/Paitesanshi/LLM-Agent-Survey/blob/main/url

Thank you for providing this comprehensive and outstanding survey.

Is it possible that "argmax" should be used instead of "argmin" in Equation 1 on page 6?

I have a quick question regarding your citations.
I've noticed that a significant proportion of your citations (over 90%) are the arXiv version of papers.
But many of them have already been published.
I'm curious about why.

Suspicion Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4

Category: Imperfect Information Game, Psychology

Project Demo
Game Data Replay
Code

First, I really thank you for the contribution that you guys have made. I am still reading it. BTW, no offense, I notice some commas are missing in the [165] reference.:joy:

Dear Authors,

Thank you for your efforts in proposing this survey paper.

We are the authors of “Chameleon,” a framework designed to seamlessly integrate LLM agents with various external tools (https://arxiv.org/abs/2304.09842, https://github.com/lupantech/chameleon-llm). Since its release in April 2023, our work has attracted significant attention from the AI research community.

We would be honored if you could include our work in the github repo (and a discussion in the next revision of your paper if it is possible). We believe that our framework complements the discussions in your work and could offer additional insights to the readers.

We thank you for considering our request and look forward to your positive response.

Hi!

This list is an invaluable resource in the area of building intelligent agents with LLMs.

I wanted to take a moment to bring your attention to a recent NeurIPS-23 paper from our lab: Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning. Instead of getting plans from LLMs directly, it allows the agent to use external planners to reliably search for plans (somewhat in a similar vein to tool-augmented LLMs).

We would be grateful if you would consider including our papers in your survey. We believe it would greatly benefit the readers interested in this burgeoning area of LLM-driven intelligent agents.

Best regards

能否增加一个信息，是否为多智能体还是单智能体