大型语言模型是否显示了类似于人类的决策启发式? 一个使用GPT-3.5的案例研究
Gaurav Suri1, Lily R Slater1, Ali Ziaee1
1Department of Psychology, San Francisco State University.
Journal of experimental psychology. General
|February 8, 2024
概括
像ChatGPT这样的大型语言模型 (LLM) 呈现出类似于人类的认知偏见,例如定和框架效应. 这表明语言本身可能会影响这些上下文敏感的反应.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 大型语言模型 (LLM) 是在广泛的数据上训练的人工智能系统,以生成类似人类的文本.
- 发电预训练变压器 (GPT) -3.5,为ChatGPT提供动力,是LLM的一个突出例子.
- 众所周知,人类的决策受认知启发学和情境敏感反应的影响.
研究的目的:
- 调查ChatGPT是否表现出启发式和上下文敏感的响应.
- 将ChatGPT的响应与使用相同提示的人类参与者的响应进行比较.
- 探索语言在认知偏见中的作用.
主要方法:
- 通过使用新的提示来测试ChatGPT和人类参与者进行了四项研究.
- 研究1研究了使用随机数值的定效应.
- 第二项研究评估了代表性和可用性启发式.
- 研究3研究了对正面和负面呈现的信息的框架效应.
- 第4项研究通过比较拥有的与新发现的物品来探索赋予效应.
主要成果:
- 聊天GPT展示了定效应,受到随机数值的影响.
- 聊天GPT表现出代表性和可用性启发式,高估了联合概率.
- 聊天GPT显示了一个框架效应,根据呈现方式对项目进行了不同的估值.
- 聊天GPT显示了一个赋予效应,比同样的新项目更高地估值了所拥有的项目.
- 在所有研究中,人类参与者表现出类似的启发式和上下文敏感的反应.
结论:
- 像ChatGPT这样的LLM可以表现出认知偏见和情境敏感反应,反映出人类的行为.
- 这些发现表明,语言的结构和内容可能在产生这些影响方面发挥着重要作用.
- 这项研究为了解语言,认知和AI行为之间的相互作用开辟了道路.
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