mlsanityData to 7 Oct 2026

Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review

arXiv 2401.01519 ↗First posted 3 Jan 2024cs.LG cs.AIcited by 17

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Abstract (arXiv)

This paper explores the frontiers of large language models (LLMs) in psychology applications. Psychology has undergone several theoretical changes, and the current use of Artificial Intelligence (AI) and Machine Learning, particularly LLMs, promises to open up new research directions. We provide a detailed exploration of how LLMs like ChatGPT are transforming psychological research. It discusses the impact of LLMs across various branches of psychology, including cognitive and behavioral, clinical and counseling, educational and developmental, and social and cultural psychology, highlighting their potential to simulate aspects of human cognition and behavior. The paper delves into the capabilities of these models to emulate human-like text generation, offering innovative tools for literature review, hypothesis generation, experimental design, experimental subjects, data analysis, academic writing, and peer review in psychology. While LLMs are essential in advancing research methodologies in psychology, the paper also cautions about their technical and ethical challenges. There are issues like data privacy, the ethical implications of using LLMs in psychological research, and the need for a deeper understanding of these models' limitations. Researchers should responsibly use LLMs in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, the article provides a comprehensive overview of the current state of LLMs in psychology, exploring potential benefits and challenges. It serves as a call to action for researchers to leverage LLMs' advantages responsibly while addressing associated risks.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 62 of its 162 references that are in the map. First posted on arXiv 3 Jan 2024.

Builds on

62 of 162 references.

References · 62

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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38 more
  1. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
  2. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 506
  3. 2022Using cognitive psychology to understand GPT-3cited by 389
  4. 2023In-Context Learning Creates Task Vectorscited by 302
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  6. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  7. 2022Emergent Analogical Reasoning in Large Language Modelscited by 239
  8. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 221
  9. 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
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  13. 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Modelscited by 159
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 7; 2026: 3.

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  1. 2024LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptionscited by 45
  2. 2025Enhanced Large Language Models for Effective Screening of Depression and Anxietycited by 9
  3. 2025Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Researchcited by 9
  4. 2025TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioningcited by 6
  5. 2025Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agentscited by 6
  6. 2025Large Language Models as Psychological Simulators: A Methodological Guidecited by 6
  7. 2024PsyDI: Towards a Personalized and Progressively In-depth Chatbot for Psychological Measurementscited by 5
  8. 2024Analyzing Nobel Prize Literature with Large Language Modelscited by 4
  9. 2025Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulationcited by 3
  10. 2024PsychoLex: Unveiling the Psychological Mind of Large Language Modelscited by 2
  11. 2024The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skillscited by 1
  12. 2024Quantifying Risk Propensities of Large Language Models: Ethical Focus and Bias Detection through Role-Playcited by 1
5 more
  1. 2024Fine-Tuning Open-Weight Language Models to Deliver Cognitive Behavioral Therapy for Depression: A Feasibility Studycited by 1
  2. 2025How Private is Your Attention? Bridging Privacy with In-Context Learningcited by 0
  3. 2026Statistical realism is not evidence that LLMs can estimate treatment effects in social science experimentscited by 0
  4. 2026A Hormone-inspired Emotion Layer for Transformer language models (HELT)cited by 0
  5. 2026Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Componentscited by 0