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Congratulations on your 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.

Hi, what a fantastic resource for developing intelligent LLM agents!

I wanted to highlight a recent paper presented at ACL 2024 Findings: TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models.
This study focuses on assessing hallucinations in role-playing LLM agents when they simulate characters at specific moments in time.

We would greatly appreciate it if you could consider adding our paper to your survey.
Thanks!

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

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: Empowering Large Language Model Agents through Action Learning
• Code: LearnAct GitHub Repository

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!

Hi! The list is indeed a valuable reference for studying and developing LLM-powered agents.
I want to introduce our recent work: RestGPT: Connecting Large Language Models with Real-World Applications via RESTful APIs (http://arxiv.org/abs/2306.06624). In this paper, we consider a more realistic scenario, connecting LLMs with RESTful APIs, which use the commonly adopted REST software architectural style for web service development. For example, RestGPT can connect with Spotify music player and solve user queries, such as “Add Summertime Sadness by Lana Del Rey in my first playlist”. In RestGPT, several agents, i.e., planner, API selector, and executor, work together to accomplish realistic complex tasks. The code and demo of our work will be released in this month.
I would be honored if RestGPT could be considered for inclusion in your list. I believe that it would greatly benefit researchers, and developers seeking to explore the practical applications of LLMs.
Best regards!

Thank you for your excellent paper list. Could you please add our AgentBoard Benchmark？ Thank you very much!
Paper: https://arxiv.org/abs/2401.13178
Code: https://github.com/hkust-nlp/AgentBoard/tree/main