The Mind in the Machine: A Survey of Incorporating Psychological Theories in LLMs
It addresses the need for integrating psychology into NLP to improve LLMs, but as a survey, it is incremental in synthesizing existing insights rather than proposing new methods.
This paper surveys how psychological theories can inform and enhance the development of Large Language Models (LLMs) across stages like data, pre-training, and evaluation, aiming to bridge disciplinary divides for more human-like cognition and interaction.
Psychological insights have long shaped pivotal NLP breakthroughs, including the cognitive underpinnings of attention mechanisms, formative reinforcement learning, and Theory of Mind-inspired social modeling. As Large Language Models (LLMs) continue to grow in scale and complexity, there is a rising consensus that psychology is essential for capturing human-like cognition, behavior, and interaction. This paper reviews how psychological theories can inform and enhance stages of LLM development, including data, pre-training, post-training, and evaluation\&application. Our survey integrates insights from cognitive, developmental, behavioral, social, personality psychology, and psycholinguistics. Our analysis highlights current trends and gaps in how psychological theories are applied. By examining both cross-domain connections and points of tension, we aim to bridge disciplinary divides and promote more thoughtful integration of psychology into future NLP research.