hyp1231 / awesome-llm-powered-agent Goto Github PK

Great collection! See how you like ReWOO since it's parallel to ReAct.
https://github.com/billxbf/ReWOO

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.

Dear authors,

We hope this comment finds you well. We wanted to take a moment to bring your attention to a relevant paper from our lab that has recently been accepted to NeurIPS 2023:

Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning — The key idea of this work is to extract an explicit domain with LLM and thereby allow the agent to use external symbolic task planner.

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 agents.

Best regards
Lin