Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Due to rapid technological advances and their extreme versatility, LLMs nowadays have millions of users and are at the cusp of being the main go-to technology for information retrieval, content generation, problem-solving, etc. Therefore, it is of great importance to thoroughly assess and scrutinize their capabilities. Due to increasingly complex and novel behavioral patterns in current LLMs, this can be done by treating them as participants in psychology experiments that were originally designed to test humans. For this purpose, the paper introduces a new field of research called "machine psychology". The paper outlines how different subfields of psychology can inform behavioral tests for LLMs. It defines methodological standards for machine psychology research, especially by focusing on policies for prompt designs. Additionally, it describes how behavioral patterns discovered in LLMs are to be interpreted. In sum, machine psychology aims to discover emergent abilities in LLMs that cannot be detected by most traditional natural language processing benchmarks.

翻译：大型语言模型（LLMs）当前正处于人工智能系统与人类沟通及日常生活深度融合的前沿。由于技术飞速进步及其极端通用性，LLMs如今拥有数百万用户，并即将成为信息检索、内容生成、问题解决等领域的主流技术。因此，全面评估与审视其能力至关重要。针对当前LLMs日益复杂和新颖的行为模式，可将它们视为原本设计用于测试人类的心理学实验中的被试者。为此，本文引入了一个名为"机器心理学"的新研究领域。文章阐述了心理学各子领域如何为LLMs的行为测试提供理论依据，界定了机器心理学研究的方法论标准，尤其聚焦于提示词设计的规范。同时，本文描述了如何解释LLMs中发现的各类行为模式。总之，机器心理学旨在揭示传统自然语言处理基准测试难以检测到的LLMs涌现能力。