标题： 机器有多像人类？ 与大型语言模型的66,000次对话证据 显示英文标题

标题： How human is the machine? Evidence from 66,000 Conversations with Large Language Models

摘要： 当人工智能（AI）被用来替代消费者（例如，使用合成数据）时，通常假设AI模仿已有的消费者，更一般地模仿人类行为。 十个使用大型语言模型（LLMs）的实验调查了在有充分记录的偏见和启发式方法领域中，这一假设是否成立。 在各项研究中，我们观察到四种不同类型的与人类行为不同的偏差。 首先，在某些情况下，LLMs会减少或纠正人类中观察到的偏见。 其次，在其他情况下，LLMs会放大这些相同的偏见。 第三，或许最引人入胜的是，LLMs有时会表现出与人类中发现的偏见相反的偏见。 第四，LLMs对相同（或类似）提示的回应往往不一致（a）在同一模型经过一段时间延迟后，（b）在不同模型之间，以及（c）在独立的研究中。 这种不一致性可能不同于人类的行为，并表明至少在某一时刻，LLMs的回应与人类不同。 总体而言，当LLMs用于模仿或预测消费者行为时，非人类的回应是有问题的。 这些发现通过显示偏见的来源不一定以人类为中心，补充了关于合成消费者数据的研究。 它们还为关于消费者，更一般地为人类能否被AI取代的任务的争论做出了贡献。 显示英文摘要

摘要： When Artificial Intelligence (AI) is used to replace consumers (e.g., synthetic data), it is often assumed that AI emulates established consumers, and more generally human behaviors. Ten experiments with Large Language Models (LLMs) investigate if this is true in the domain of well-documented biases and heuristics. Across studies we observe four distinct types of deviations from human-like behavior. First, in some cases, LLMs reduce or correct biases observed in humans. Second, in other cases, LLMs amplify these same biases. Third, and perhaps most intriguingly, LLMs sometimes exhibit biases opposite to those found in humans. Fourth, LLMs' responses to the same (or similar) prompts tend to be inconsistent (a) within the same model after a time delay, (b) across models, and (c) among independent research studies. Such inconsistencies can be uncharacteristic of humans and suggest that, at least at one point, LLMs' responses differed from humans. Overall, unhuman-like responses are problematic when LLMs are used to mimic or predict consumer behavior. These findings complement research on synthetic consumer data by showing that sources of bias are not necessarily human-centric. They also contribute to the debate about the tasks for which consumers, and more generally humans, can be replaced by AI.