A Large Language Model (LLM) is an artificial intelligence system that has been trained on vast amounts of natural language data, enabling it to generate human-like responses to written or spoken language input. GPT-3.5 is an example of an LLM that supports a conversational agent called ChatGPT. In this work, we used a series of novel prompts to determine whether ChatGPT shows heuristics, biases, and other decision effects. We also tested the same prompts on human participants. Across four studies, we found that ChatGPT was influenced by random anchors in making estimates (Anchoring Heuristic, Study 1); it judged the likelihood of two events occurring together to be higher than the likelihood of either event occurring alone, and it was erroneously influenced by salient anecdotal information (Representativeness and Availability Heuristic, Study 2); it found an item to be more efficacious when its features were presented positively rather than negatively - even though both presentations contained identical information (Framing Effect, Study 3); and it valued an owned item more than a newly found item even though the two items were identical (Endowment Effect, Study 4). In each study, human participants showed similar effects. Heuristics and related decision effects in humans are thought to be driven by cognitive and affective processes such as loss aversion and effort reduction. The fact that an LLM - which lacks these processes - also shows such effects invites consideration of the possibility that language may play a role in generating these effects in humans.

研究人员使用一系列新颖的提示来测试 ChatGPT 中是否显示出启发式方法、偏见和其他决策效应等现象，并发现 ChatGPT 在这些效应中表现出与人类相似的行为。

大型语言模型是否表现出类似于人类的决策启发式？以GPT-3.5为例的案例研究