论文标题
快速思考大型语言模型
Thinking Fast and Slow in Large Language Models
论文作者
论文摘要
大型语言模型（LLM）目前处于将AI系统与人类交流和日常生活相结合的最前沿。因此，评估其新兴能力非常重要。在这项研究中，我们表明像GPT-3这样的LLM表现出与人类般的直觉相似的行为，以及随之而来的认知错误。但是，具有较高认知能力的LLM，尤其是ChatGpt和GPT-4，学会了避免屈服于这些错误并以超值方式执行。在我们的实验中，我们使用认知反射测试（CRT）以及最初旨在研究人类直观决策的语义幻想进行探测。我们的研究表明，通过心理学方法研究LLM的LLM有可能揭示其他未知的紧急特征。
Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate their emerging abilities. In this study, we show that LLMs like GPT-3 exhibit behavior that strikingly resembles human-like intuition - and the cognitive errors that come with it. However, LLMs with higher cognitive capabilities, in particular ChatGPT and GPT-4, learned to avoid succumbing to these errors and perform in a hyperrational manner. For our experiments, we probe LLMs with the Cognitive Reflection Test (CRT) as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Our study demonstrates that investigating LLMs with methods from psychology has the potential to reveal otherwise unknown emergent traits.
