# Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects arXiv: https://arxiv.org/abs/2312.04578 ยท HTML: https://mlsanity.com/paper/2312.04578/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 42 of its 100 references that are in the map. - First posted on arXiv 1 Dec 2023. ## Builds on (most cited first, 30 of 42) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021) - [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023) - [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022) - [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://mlsanity.com/paper/2306.11698/) (2023) - [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022) - [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021) - [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023) - [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022) - [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023) - [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023) - [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023) - [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023) - [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023) - [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022) - [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023) - [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023) - [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023) - [Large Language Models are Few-Shot Health Learners](https://mlsanity.com/paper/2305.15525/) (2023) - [Towards artificial general intelligence via a multimodal foundation model](https://mlsanity.com/paper/2110.14378/) (2021) - [Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support](https://mlsanity.com/paper/2307.15810/) (2023) - [GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://mlsanity.com/paper/2308.12488/) (2023) - [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023) - [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023) - [Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks](https://mlsanity.com/paper/2205.05718/) (2022) - [Evaluation of ChatGPT for NLP-based Mental Health Applications](https://mlsanity.com/paper/2303.15727/) (2023) - [CGMI: Configurable General Multi-Agent Interaction Framework](https://mlsanity.com/paper/2308.12503/) (2023) - [Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT](https://mlsanity.com/paper/2303.03186/) (2023) ## Built on by (most cited first, 8 of 8) - [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024) - [Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts](https://mlsanity.com/paper/2403.13786/) (2024) - [Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification](https://mlsanity.com/paper/2402.07595/) (2024) - [Deep Learning and Large Language Models for Audio and Text Analysis in Predicting Suicidal Acts in Chinese Psychological Support Hotlines](https://mlsanity.com/paper/2409.06164/) (2024) - [AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts](https://mlsanity.com/paper/2404.11449/) (2024) - [SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis](https://mlsanity.com/paper/2404.12659/) (2024) - [Fine-grained Speech Sentiment Analysis in Chinese Psychological Support Hotlines Based on Large-scale Pre-trained Model](https://mlsanity.com/paper/2405.04128/) (2024) - [Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence](https://mlsanity.com/paper/2506.23503/) (2025) ## Abstract (arXiv) The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications.