The Mind in the Machine: A Survey of Incorporating Psychological Theories in LLMs (2505.00003v1)
Abstract: 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 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.
- Zizhou Liu (5 papers)
- Ziwei Gong (10 papers)
- Lin Ai (15 papers)
- Zheng Hui (27 papers)
- Run Chen (10 papers)
- Colin Wayne Leach (2 papers)
- Michelle R. Greene (5 papers)
- Julia Hirschberg (37 papers)