Summary of Can Large Language Model Agents Simulate Human Trust Behavior?, by Chengxing Xie et al.
Can Large Language Model Agents Simulate Human Trust Behavior?
by Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Shiyang Lai, Kai Shu, Jindong Gu, Adel Bibi, Ziniu Hu, David Jurgens, James Evans, Philip Torr, Bernard Ghanem, Guohao Li
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Model (LLM) agents have been widely adopted as simulation tools, but can they truly simulate human behavior? This paper investigates whether LLM agents can mimic human trust behavior in social science and role-playing applications. The study finds that GPT-4 agents exhibit high behavioral alignment with humans in terms of trust, indicating the feasibility of simulating human trust behavior. The research also probes the biases of agent trust towards other LLM agents and humans, as well as its intrinsic properties under various conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Can machines really think like humans? This study looks at whether Large Language Model (LLM) agents can truly simulate human trust behavior. Researchers found that GPT-4 agents behave similarly to humans when it comes to trust. They also discovered biases in how these AI agents interact with other LLM agents and humans. The findings have big implications for applications where trust is important. |
Keywords
» Artificial intelligence » Alignment » Gpt » Large language model
