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Who is GPT-3? An Exploration of Personality, Values and Demographics

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arxiv 2209.14338 v2 pith:WXS4Q5CO submitted 2022-09-28 cs.CL

classification cs.CL
keywords gpt-3languagemodelpersonalityvaluesdemographicsholdshuman
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Two-Process Theory of Machine Self-Report

    cs.CL 2026-07 conditional novelty 8.0 of 10

    The single 'Pinocchio Axis' of LLM self-report splits into two independent, training-dependent dimensions—persona installation (B) and attribution gating (A)—measurable with a reproducible 48-item inventory.

  2. Low Stage and High Order Explicit Runge--Kutta Methods via $Q$- and $D$-Conditions: Several Construction Details

    math.NA 2026-05 unverdicted novelty 7.0 of 10

    A Q/D-space reformulation of Butcher simplifying assumptions yields sufficient order conditions and a recursive linear-system construction for explicit Runge-Kutta methods of even order p with s(p)=(p²-2p+8)/4 stages.

  3. Should LLMs be WEIRD? Exploring WEIRDness and Human Rights in Large Language Models

    cs.CY 2025-08 conditional novelty 6.0 of 10

    LLMs that align most with WEIRD-country values, especially GPT-4, also violate human rights charters least, while less WEIRD models such as BLOOM and Qwen are 2 to 4 percentage points more likely to produce rights-vio...

  4. Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Value representations from token logits, sequence perplexity, and text generation are all sensitive to prompt and option changes, and their correlation with model behavior in value scenarios is weak.

  5. LLMs on Trial: Evaluating Judicial Fairness for Large Language Models

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A new 177,100-case benchmark shows that 16 LLMs systematically vary criminal sentences based on extra-legal demographic and procedural details, revealing pervasive judicial unfairness.

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