Who is GPT-3? An Exploration of Personality, Values and Demographics
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Abstract (arXiv)
Language models such as GPT-3 have caused a furore in the research community. Some studies found that GPT-3 has some creative abilities and makes mistakes that are on par with human behaviour. This paper answers a related question: Who is GPT-3? We administered two validated measurement tools to GPT-3 to assess its personality, the values it holds and its self-reported demographics. Our results show that GPT-3 scores similarly to human samples in terms of personality and - when provided with a model response memory - in terms of the values it holds. We provide the first evidence of psychological assessment of the GPT-3 model and thereby add to our understanding of this language model. We close with suggestions for future research that moves social science closer to language models and vice versa.
Key facts. Cited by 112 papers in the map (data through 14 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 28 Sep 2022.
Builds on
6 of 21 references.
References · 6
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 713
- 2022Using cognitive psychology to understand GPT-3cited by 398
- 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 253
- 2021Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoningcited by 85
- 2022Putting GPT-3's Creativity to the (Alternative Uses) Testcited by 62
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Citing papers per year: 2023: 30; 2024: 37; 2025: 26; 2026: 19.
112 papers.
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- 2026ValueFlow: Measuring the Propagation of Value Perturbations in Multi-Agent LLM Systems9 Feb 2026
- 2026HumanStudy-Bench: Towards AI Agent Design for Participant Simulation31 Jan 2026
- 2025Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents1 Aug 2025
- 2025Modeling Earth-Scale Human-Like Societies with One Billion Agents7 Jun 2025
- 2025Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications30 Apr 2025
- 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents4 Dec 2024
- 2024Is persona enough for personality? Using ChatGPT to reconstruct an agent's latent personality from simple descriptions18 Jun 2024
- 2024From Persona to Personalization: A Survey on Role-Playing Language Agents28 Apr 2024
- 2024Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models13 Jan 2024
- 2023Do LLM Agents Exhibit Social Behavior?23 Dec 2023
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- 2023Language Model Behavior: A Comprehensive Surveycited by 92
- 2023Moral Foundations of Large Language Modelscited by 85
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- 2026The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment6 Jul 2026
- 2026ActTraitBench: Quantifying the Knowledge-Decision Gap in Large Language Models via Human-Grounded Behavioral Validation28 May 2026
- 2026Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?16 May 2026
- 2026The Shared Latent Structure of LLM Psychometric Responses6 May 2026
- 2026Event-Centric Human Value Understanding in News-Domain Texts: An Actor-Conditioned, Multi-Granularity Benchmark18 Mar 2026
- 2026Elder-Sim: A Psychometrically Validated Platform for Personality-Stable Elderly Digital Twins16 Mar 2026
- 2026Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook16 Mar 2026
- 2026Fusian: Multi-LoRA Fusion for Fine-Grained Continuous MBTI Personality Control in Large Language Models16 Mar 2026
- 2026Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues6 Mar 2026
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