llm-agent-survey's People
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neozoik githungdang vishalbelsare sarahbrownplace eltociear sml8648 kalchakra13 hainan89 apollohuang1 leejodie bingtian88 yomaser dunake sigma-lm hangxue-lab ericwang1104 ai-jie01 soon14 syaikhipin keisuke-yamamotoo suprah925 taner45 shanshanqwer dayadaya222 hhy5277 thanhpham1987 jeffrey-m-johnson dattgoswami csshali tony2cmu f901107 m1n9x boyuanzheng010 yuansky zhouxh19 play201861 andy-jqa gayansamuditha sundogs8603 vasco989k kaifahmad1 bharatr21 drasaadmoosa agpascoe yushengsu-thu nehaa28 vital121 dogank01 sxhxliang lcreateai bharathreddyinsightaiq iphyer sreenivasanac utathya samueldinesh zxf864823150 zhangzhuobys malxhn asuzukosi sword865 liudingxiao hillviewcap zhihao-chen fai247 sairamakrishnapittu xxupiano llv22 maociao xuzhouwang zongdaoming akayalml lgssstsp brunotech koalazf99 xingyaoww sorokinvld deepcharle tuanzi1015 ai-dialogos-chatbot-with-llms pdragonlabs sinianyutian jamesliu dianadanzhang thzdyjy singl3 851628647 yaokaifei al-377 corny813 kelizhang hertera1 cicimmmmm oztc superfang123 tinker713 xiaoyeye668 nlzracbwq9 ngduyanhece flywolfs twosugar666llm-agent-survey's Issues
Not an issue, just wanted to say thank you for keeping this up to date
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
Missing Related Work
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.
Pointer to early LLM-based simulation agent
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
APi-Bank URLs are dead
The URLs for paper "API-Bank" are missing.
Currently they point to https://github.com/Paitesanshi/LLM-Agent-Survey/blob/main/url
Equation 1
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?
citation version
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.
[165] reference
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:
Introducing our NeurIPS 2023 paper
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
unified agent framework
Hello, will the unified agent framework mentioned in the article be open source?
多智能体
能否增加一个信息,是否为多智能体还是单智能体
Update Chart
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?
I'd like to share recent work "Empowering Large Language Model Agents through Action Learning"
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!
One reference on LLM Agents playing Trust Games
Congratulations on your recent solid survey paper and impressive paper list!
We have a related paper on LLM Agents playing Trust Games.
Can Large Language Model Agents Simulate Human Trust Behaviors?
- arxiv : https://arxiv.org/abs/2402.04559
- code : https://github.com/camel-ai/agent-trust
- project website : https://www.camel-ai.org/research/agent-trust
- We discover the trust behaviors of LLM agents under the framework of Trust Games, and the high behavioral alignment between LLM agents and humans regarding the trust behaviors, particularly for GPT-4, indicating the feasibility to simulate human trust behaviors with LLM agents.
- abstract: Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in applications such as social science. However, one fundamental question remains: can LLM agents really simulate human behaviors? In this paper, we focus on one of the most critical behaviors in human interactions, trust, and aim to investigate whether or not LLM agents can simulate human trust behaviors. We first find that LLM agents generally exhibit trust behaviors, referred to as agent trust, under the framework of Trust Games, which are widely recognized in behavioral economics. Then, we discover that LLM agents can have high behavioral alignment with humans regarding trust behaviors, particularly for GPT-4, indicating the feasibility to simulate human trust behaviors with LLM agents. In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans. We also explore the intrinsic properties of agent trust under conditions including advanced reasoning strategies and external manipulations. We further offer important implications of our discoveries for various scenarios where trust is paramount. Our study provides new insights into the behaviors of LLM agents and the fundamental analogy between LLMs and humans.
能否在交互式表格中加入是否需要finetune这列呀
Add Suspicion-Agent
Suspicion Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4
Category: Imperfect Information Game, Psychology
AppAgent
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