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【第88期】LLM Agent能否模拟人的信任行为？
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Can Large Language Model Agents Simulate Human Trust Behavior?Summary
This research paper investigates whether Large Language Models (LLMs) can simulate human trust behavior. Using Trust Games, the study finds that LLMs, particularly GPT-4, exhibit trust behaviors aligning significantly with human patterns, demonstrating a high degree of behavioral alignment. The research also explores biases in LLM trust behavior, the impact of external manipulation and reasoning strategies on LLM trust, and the implications for human simulation, agent cooperation, and human-agent collaboration. The findings suggest considerable potential for using LLMs to simulate human social interactions but also highlight potential limitations and risks. The study provides a framework for understanding the analogy between LLMs and human behavior beyond value alignment.
这篇研究论文探讨了大型语言模型（LLMs）是否能够模拟人类的信任行为。通过使用信任游戏，该研究发现，LLMs，特别是GPT-4，表现出与人类行为模式显著一致的信任行为，展现了高度的行为一致性。研究还探讨了LLMs信任行为中的偏见、外部操控和推理策略对LLMs信任行为的影响，以及这些对人类模拟、智能体合作和人机协作的意义。研究结果表明，LLMs在模拟人类社会互动方面具有相当大的潜力，同时也指出了其可能的局限性和风险。该研究为理解LLMs与人类行为之间的类比关系提供了一个超越价值对齐的框架。
原文链接：https://arxiv.org/abs/2402.04559
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Can Large Language Model Agents Simulate Human Trust Behavior?Summary
This research paper investigates whether Large Language Models (LLMs) can simulate human trust behavior. Using Trust Games, the study finds that LLMs, particularly GPT-4, exhibit trust behaviors aligning significantly with human patterns, demonstrating a high degree of behavioral alignment. The research also explores biases in LLM trust behavior, the impact of external manipulation and reasoning strategies on LLM trust, and the implications for human simulation, agent cooperation, and human-agent collaboration. The findings suggest considerable potential for using LLMs to simulate human social interactions but also highlight potential limitations and risks. The study provides a framework for understanding the analogy between LLMs and human behavior beyond value alignment.
这篇研究论文探讨了大型语言模型（LLMs）是否能够模拟人类的信任行为。通过使用信任游戏，该研究发现，LLMs，特别是GPT-4，表现出与人类行为模式显著一致的信任行为，展现了高度的行为一致性。研究还探讨了LLMs信任行为中的偏见、外部操控和推理策略对LLMs信任行为的影响，以及这些对人类模拟、智能体合作和人机协作的意义。研究结果表明，LLMs在模拟人类社会互动方面具有相当大的潜力，同时也指出了其可能的局限性和风险。该研究为理解LLMs与人类行为之间的类比关系提供了一个超越价值对齐的框架。
原文链接：https://arxiv.org/abs/2402.04559