This study does not measure an intrinsic GPT-3 personality. It investigates how mostly university participants perceived personality in English conversations generated by text-davinci-002 in October-November 2022. In the first experiment, 86 participants interacted for three weeks with three personas and supplied 9,267 three-adjective self-reports. The system also injected one of six emotional states, one of four conversation starts and the user's own messages; the observed object is therefore a prompt-conditioned, co-produced conversation rather than the model in isolation.
The 27,801 adjective entries yielded 2,999 unique terms and then 147 descriptors through consequential construction choices: manual phrase reduction, manual translation of 104 terms, Merriam-Webster correction, removal of negation prefixes, screening against prior lists, greedy frequency-ordered synonym clustering and an elbow-chosen t=10 cutoff. The result is a curated instrument, not a neutral extraction or automated summary of participant language. The official supplement publishes all descriptors, loadings and Big Five correspondences, but not the intermediate decisions.
A second open survey recruited 556 people and retained 425 after excluding 131 (23.5%): nine for not using all four categories and 122 for finishing outside 8-25 minutes. Each participant read a different random set of three conversations and rated all 147 adjectives once for the bundle's overall chatbot personality. Exploratory factor analysis reports KMO=.896, but the retention rules do not select eight factors: Kaiser suggests 34 and the scree plot four or six. The authors analyze five for Big Five comparison, ten for earlier-work comparison and choose eight as an intermediate, interpretable value. That solution explains 38.2% of variance and is not confirmed in another sample.
The factors are subjectively labeled decency, profoundness, instability, vibrancy, engagement, neuroticism, serviceability and subservience. They form a plausible exploratory taxonomy of perceptions, but each vector mixes one rater and three different conversations; covariance can incorporate content, prompts, users, bundle selection and response styles. There is no multilevel analysis, parallel analysis, confirmation split, CFA, stability bootstrap or external replication.
The most prominent reliability figure requires an important correction. The main text presents alpha=.78 for people who saw the same three conversations, but Appendix B shows .57 without an ordering restriction and only 23 rater pairs. The .78 value additionally requires the same conversation to appear last and is based on nine pairs. Alpha=.31 among more than 80,000 pairs sharing no conversation is not interrater reliability of the same object; it is profile similarity across different stimuli and can reflect adjective base rates or response styles.
Big Five agreement is not convergent validation either. After list matching, a psychologist manually assigned 95 of 147 adjectives and 24 remained unmatched; “agreement” is the percentage of a factor's words coded to each trait, not correlation with a Big Five instrument administered on the same stimuli. Comparison with voice assistants likewise uses another study, sample and vocabulary; measurable overlaps are 27-41% and do not isolate technological progress.
The faithful conclusion is that participants in 2022 organized perceptions of these particular conversations into several interpretable social, emotional and functional dimensions. The study contributes an unusual longitudinal design, identifies the model and settings and publishes complete tables. It does not validate a stable GPT-3 personality, a universal eight-factor taxonomy, psychometric failure of Big Five, prompt independence or generalization to ChatGPT, GPT-4 or current models. Numerical reproduction is also impossible: official pages and the ETH record expose PDFs, BibTeX and metadata, but no conversations, rating matrix, exclusions, code, environment or immutable release.