This preprint provides a broad narrative review of how psychological theories and instruments are transferred to the study of LLMs. It organizes 154 references across six areas: assessment tools, LLM-specific datasets, consistency and stability, findings attributed to model families, methods for inducing or editing personality, and simulations of human behavior. It describes personality questionnaires such as MBTI, BFI, IPIP-NEO, SD3, HEXACO, and SSCS; affect and aggression measures such as PANAS and BPAQ; and theory-of-mind tasks including false-belief tests, Strange Stories, Imposing Memory, and Faux Pas. It also collects model-oriented or repurposed resources including MPI, TRAIT, SECEU, EQ-Bench, EmoBench, ToMi, a multidomain psychometrics benchmark, and SocialIQA.
Its most useful contribution is taxonomic. It distinguishes response consistency under changes in option order, prompts, temperature, wording, reverse-scored items, or repeated administration; statistical consistency through standard deviation, alpha, omega, ICC, and test-retest; and agreement between self-report and behavior. It separates these notions from stability after fine-tuning, parameter changes, or personality induction and editing, while acknowledging that the literature uses “stability” inconsistently. For trait simulation it summarizes explicit prompting, implicit scenarios, fine-tuning, and model editing; for applications it distinguishes social experiments, games, and interactive negotiation. This vocabulary helps avoid treating a questionnaire, a social-capability benchmark, and agent behavior as equivalent.
The profiles of GPT, LLaMA, Mistral, Qwen, Claude, and Gemini, however, do not come from a shared experiment or meta-analysis. They combine different generations, sizes, versions, languages, prompts, instruments, and dates. The paper itself reports contradictory GPT-4 SD3 results and task-dependent variability, yet the opening figure compresses each family into one “Psychological ID,” and the conclusion says GPT-4 performs best across all dimensions. No common model-by-test-by-condition matrix supports that comparison. Serving as an EQ-Bench judge, succeeding on an emotion task, producing empathic language, and answering a self-report questionnaire also do not establish the same psychological construct. Several claims in the Claude, Gemini, and Qwen sections lack a direct citation or turn how a model was used into evidence of ability.
The central limitation is that the paper calls itself systematic without reporting a systematic-review method. It gives no bibliographic databases, search string, search period, cut-off date, inclusion or exclusion criteria, screening process, flow diagram, included-study corpus, extraction protocol, reviewers, disagreement procedure, or quality and risk-of-bias assessment. The 154 references mix primary LLM studies, human psychometric validation studies, reviews, model web pages, and general resources; they do not form a defined evidence set another team could reconstruct. Reliability or validity in human respondents also does not automatically transfer to a model: saying that MBTI has a seventh-grade reading requirement only indicates that an LLM can process the text, not that its scores are valid, invariant, or interpretable as personality.
The paper appropriately warns about sensitivity to prompts, option order, temperature, limited diversity, and mismatches between human instruments and models. It also acknowledges that LLMs cannot fully replace human participants. Its proposals to improve safety by “optimizing” traits and to use models as human samples nevertheless remain hypotheses, not findings established by the review. There is no new experiment, meta-analysis, extraction table, code, or data; the official source archive contains TeX, bibliography, and seven figures. The defensible contribution is a broad, visual entry point to tools, datasets, and applications through April 2025. It is not a systematic estimate of the evidence, does not establish stable family-level psychological profiles, and does not validate clinical or population-level uses of these measurements.