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 \textquotedblmachine psychology\textquotedbl. 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.
%0 Journal Article
%1 Hagendorff.2023e
%A Hagendorff, Thilo
%D 2023
%J arXiv
%K behavior;psychology language large models;machine
%P 1--15
%T Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods
%X 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 \textquotedblmachine psychology\textquotedbl. 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.
@article{Hagendorff.2023e,
abstract = {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 {\textquotedbl}machine psychology{\textquotedbl}. 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.},
added-at = {2024-01-31T09:41:13.000+0100},
author = {Hagendorff, Thilo},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/24a6e52ba8bf55a028a9b2cc92101056d/thilohagendorff},
interhash = {e9bfd0d44282a2edbbe9fc459efe8c04},
intrahash = {4a6e52ba8bf55a028a9b2cc92101056d},
journal = {arXiv},
keywords = {behavior;psychology language large models;machine},
pages = {1--15},
timestamp = {2024-03-01T12:09:30.000+0100},
title = {Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods},
year = 2023
}
