This survey offers a simple and useful distinction for organizing a literature that often mixes different meanings of “persona.” In role-playing, the persona is assigned to the model itself, a doctor, judge, programmer, or character, so that it acts inside an environment. In personalization, the persona belongs to the user, their history, preferences, context, or attributes, and the model adapts its response to that person. The paper turns this difference in ownership and purpose into a shared taxonomy, adds a section on personality evaluation, and assembles representative tasks, datasets, and papers published through 2024.
The role-playing branch is organized by environment, software development, games, medicine, and LLM-as-evaluator, by interaction schema, single agent or cooperative/adversarial multi-agent, and by behavior described as voluntary, conforming, or destructive. The personalization branch covers recommendation, search, education, healthcare, and either task-oriented or user-persona dialogue. This map helps distinguish a model performing an assigned role from a model inferring and satisfying the preferences of a real user, while recognizing that both mechanisms may coexist in one application.
The personality section surveys Big Five, MBTI, the Machine Personality Inventory, and LLM-rated interviews used to assess character alignment. Its most important point is cautionary: output that appears consistent with an assigned persona does not establish that a human psychometric questionnaire transfers validly to a model. The section is nevertheless brief and mainly descriptive. It does not appraise the psychometric quality of each study, compare instruments under common criteria, or reanalyze the evidence behind strong claims reproduced from cited work, such as MBTI types or “emergent behaviors.”
The paper should be read as a narrative survey and design map, not a systematic review or meta-analysis. It reports no searched databases, query, cutoff dates, inclusion or exclusion criteria, record counts, screening procedure, extraction process, bias assessment, or quality appraisal. It also performs no original experiment or homogeneous comparison, and explicitly acknowledges that heterogeneous metrics prevent a fair comprehensive performance table. The companion repository is a reading list, but contains only a README and images, has no license or structured data, and its latest commit is the October 2024 camera-ready update despite a promise of continuous maintenance. The defensible contribution is therefore a strong introductory taxonomy for separating model roles from user profiles, not evidence that role-playing or personalization generally makes LLMs more effective, safe, or psychometrically valid.