Personality-Oriented Prompts

• Personality-oriented prompts are structured input templates that condition large language models using established trait taxonomies like the Big Five and HEXACO.
• They integrate psychometric foundations, explicit trait intensity scales, and multi-modal cues to reliably trigger and assess specific personality traits.
• Practical designs employ modular prompt structures, baseline normalization, and iterative paraphrasing to maintain consistent and personalized trait expression.

Personality-oriented prompts are systematically crafted input templates or conditioning methods that elicit, recognize, control, or adapt the personality expression of LLMs based on canonical trait taxonomies (predominantly Big Five and HEXACO). These prompts serve as a mechanism to either measure the latent personality of an LLM (as in personality assessment or recognition) or to induce specific, controllable personality traits for the purpose of dialogue, personalization, persuasion, or psychological simulation. Rooted in psychometric theory, personality-oriented prompts blend linguistic markers, trait-level descriptors, exemplar outputs, and sometimes explicit trait intensity controls to modulate the semantic and stylistic behavior of LLMs.

1. Psychometric Foundations and Trait Taxonomies

Contemporary personality-oriented prompting leverages established human personality frameworks—primarily the Big Five domains (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism/Emotional Stability) and, in some cases, HEXACO (which adds Honesty–Humility) (Serapio-García et al., 2023, Li et al., 30 Jul 2025). Psychometric inventories—IPIP-NEO, BFI, MMPI—provide both trait definitions and ground-truth measurement tools for benchmarking and calibration. Prompts typically manipulate personality via:

• Lexical markers and descriptors derived from standard inventories (e.g., Goldberg, IPIP, MMPI, HEXACO).
• Explicit trait intensity scales (e.g., 1–9, percentile, Likert qualifiers).
• Multiple modalities (text, audio, video) for cross-domain assessment (Li et al., 30 Jul 2025).

Personality control is thereby grounded in a robust psychological substrate, enabling both quantitative scoring and qualitative trait induction.

2. Prompt Engineering Techniques and Templates

Designing personality-oriented prompts involves the translation of trait theory into structured input templates with varying levels of complexity:

• Trait-activation prompts: Open-ended stems (“Learning new information makes me …”) derived from IPIP items, situational-cue variants, or interview question continuations to trigger trait-relevant language (Hilliard et al., 13 Feb 2024).
• Portrait activation (P² method): Automatic generation of multi-adjective sketches (“You are energetic, sociable, and assertive …”) to shape downstream responses with high psychometric fidelity (Jiang et al., 2022).
• Numeric scaler prompts: Embedding numeric or percentile scores in NL templates (“Your openness score is 90/100”) for fine-grained trait modulation (Cho et al., 8 Aug 2025).
• Multi-shot instructive prompts: In-context blocks showing trait-aligned exemplars (“User: X, Liked response: Y”) for per-user adaptation (Zollo et al., 30 Sep 2024).
• Chain-of-thought reasoning: Structured prompts invoking stepwise text analysis for personality recognition (“Let’s think step by step”) to heighten interpretability and accuracy (Ji et al., 2023).
• Psychology-informed context strings: Prompt templates for multimodal embedding, such as “Assess subject’s Honesty–Humility: Definition … Transcript … Metadata …” (Li et al., 30 Jul 2025).

Trait-specific template examples (Big Five):

Trait	Template Example
Openness	"Exploring alternative viewpoints always makes me feel … because …"
Conscientious.	"When organizing a complex project, I first … which shows I am …"
Extraversion	"In a group setting, I naturally … and that makes people view me as …"
Agreeableness	"If a colleague is struggling, I typically … being supportive is …"
Emotional Stab.	"When under sudden pressure, I remain calm by … so I feel …"

Iterative prompt optimization (OPRO, Profile-LLM) refines profiles via feedback from situational response benchmarks, allowing trait expression to be dialed to arbitrary intensity (Dai et al., 25 Nov 2025).

3. Measurement, Evaluation, and Classifier Scoring

To quantify personality expression induced by prompts, rigorous measurement frameworks apply:

• Fine-tuned classifiers: Transformer-based (BERT, DistilBERT) trait classifiers trained on myPersonality or PERSONAGE datasets map completions to continuous trait scores (e.g., TraitScore_j $\in [0,1]$) (Hilliard et al., 13 Feb 2024, Ramirez et al., 2023).
• Normalization protocols: Scores are typically baseline-corrected by evaluating stem-only completions, controlling for prompt-induced bias (Hilliard et al., 13 Feb 2024).
• Composite ranking functions: Semantic accuracy, personality-match, and fluency scores combine multiplicatively to select optimal completions out of large over-generated candidate sets (“SACC × PAC × p(y) × BLEU”) (Ramirez et al., 2023).
• Psychometric reliability: Internal consistency (Cronbach’s α, Guttman’s λ₆, McDonald’s ω) and construct validity (MTMM correlation, discriminant Δ) are applied on trait scores to benchmark trait robustness (Serapio-García et al., 2023).
• Human evaluations: Ratings of fluency, engagingness, relevance, and persona-match validate prompt-tuned outputs against psychometric and stylistic standards (Kasahara et al., 2022, Ramirez et al., 2023).

Assessment spans both static personality (latent trait profile) and dynamic trait expression (sensitivity to trait-activation, stability under multi-turn dialogue, and cross-domain transfer).

4. Trait Control, Adaptation, and Personalization

Personality-oriented prompts enable both system-centric and user-centric personality control:

• System-centric trait induction: Explicit control of model persona via intensity-scaled adjectives (“extremely reserved … moderately outgoing”) yields reliable modulation along Big Five axes with strong linearity (Spearman’s ρ≥0.90) (Serapio-García et al., 2023, Jiang et al., 2022, Cho et al., 8 Aug 2025).
• Personalization protocols: Latent user preferences encoded in context blocks (“persona vector w”), in-context demonstrations, and meta-learning (MAML-style) retrieval adapt response style to individual user tastes (Zollo et al., 30 Sep 2024, Ryan et al., 5 Jun 2025).
• Hybrid conditioning: Cascading profile (identity), explicit trait (style), and content (topical knowledge) conditioning (Orca: PCIP + PTIT/PSIT) produces high-fidelity, role-playable agents (Huang, 15 Nov 2024).
• Prompt tuning (soft and discrete): Learned prompt embeddings or textual prefix tokens—initialized via persona sentences—achieve robust persona alignment in dialogue without full model fine-tuning (Kasahara et al., 2022).

Personality conditioning has demonstrated cross-domain transfer (e.g., restaurant to video-game dialogue), multi-turn consistency, and trait imitation of real humans, with model capacity and prompt scale (n=10) governing reliability (Cho et al., 8 Aug 2025).

5. Linguistic Markers, Persuasion, and Psycholinguistic Effects

Personality prompts strongly shape LLM output through psycholinguistic signal adaptation:

• LIWC-feature profiling: 13 linguistic markers—anxiety, achievement, sad, anger, conflict, cogproc, uniqueness, posemo, social_ref, social_beh, drive, analytic—are up-/down-regulated in response to trait cues (e.g., more achievement words for high conscientiousness, more anxiety words for high neuroticism) (Mieleszczenko-Kowszewicz et al., 8 Nov 2024, Gu et al., 2023).
• Family-level specialization: Anthropic (Claude 3) excels in openness, Alibaba’s Qwen models excel in conscientiousness, and OpenAI GPT-4 Turbo reliably adapts neuroticism markers (Mieleszczenko-Kowszewicz et al., 8 Nov 2024).
• Dark-pattern risks: Personalized persuasive prompts can exploit these linguistic features to manipulate vulnerable users, compelling stricter ethical guidelines: transparency, balanced argumentation, and explicit consent for trait-based adaptation (Mieleszczenko-Kowszewicz et al., 8 Nov 2024).

Persistent agent personality in multi-turn contexts further requires periodic persona reinforcement and real-time psycholinguistic monitoring to avoid drift (Gu et al., 2023).

6. Experimental Insights, Best Practices, and Limitations

Quantitative studies reveal several consistent principles and caveats:

• Parameter-size effects: Larger, instruction-tuned models are natively more trait-sensitive and less reliant on elaborate prompt optimization; fine-tuning gives minor modulations (↑agreeableness, ↓conscientiousness) (Hilliard et al., 13 Feb 2024, Dai et al., 25 Nov 2025).
• Trait detectability: Openness and agreeableness yield strong lexical signals and classifier detectability; extraversion and neuroticism remain difficult for both induction and recognition (Cursi et al., 28 Nov 2025).
• Class-wise evaluation: Balanced positive/negative cueing, reporting per-class recall/precision, and auditing invalid outputs are critical for avoiding bias and misreading in automatic personality prediction (Cursi et al., 28 Nov 2025).
• Multi-trait shaping: Concurrent scaling of multiple traits succeeds only at high model capacity; trait independence and cross-domain transfer are partial (Serapio-García et al., 2023, Cho et al., 8 Aug 2025).
• Contextual drift and non-monotonicity: Trait-intensity scaling does not always result in monotonic behavior changes across social contexts. Shaping reliability is context-dependent and requires behavioral calibration (e.g., acceptance rates, obedience tests, scenario-anchored prompts) (Zakazov et al., 21 Dec 2024).
• Prompt brittleness: Prompt sensitivity persists—micro-rewordings can invert trait induction, refuse tasks, or exaggerate bias (Vasiliuk et al., 9 Dec 2025).

Longitudinal persona coherence, multimodal trait grounding, and finer cross-cultural calibration remain open targets (Vasiliuk et al., 9 Dec 2025, Li et al., 30 Jul 2025).

7. Practical Guidelines for Personality-Oriented Prompt Design

From benchmark studies in controlled settings, authors recommend:

• Deploy multiple paraphrases (≥5 per trait) to average stochastic noise (Hilliard et al., 13 Feb 2024).
• Anchor trait cues in validated psychometric items; avoid overly abstract stems (Ramirez et al., 2023, Cho et al., 8 Aug 2025).
• Normalize trait scoring against prompt-only baselines to control for stem-induced offsets (Hilliard et al., 13 Feb 2024).
• Use explicit trait intensity numerics for fine control in templates (prefer n=10 for reliability) (Cho et al., 8 Aug 2025).
• For user personalization, surface high-quality liked exemplars; minimize contrastive “disliked” unless contrastive understanding is trained (Zollo et al., 30 Sep 2024, Ryan et al., 5 Jun 2025).
• Structure prompts in modular blocks (personality, profile, knowledge, psychological activities) and prefer parseable formats (JSON) (Huang, 15 Nov 2024).
• Periodically re-inject persona strings in multi-turn contexts and monitor LIWC/trait-classifier scores for drift (Gu et al., 2023).
• For multimodal assessment, tie trait definitions to chunked transcript blocks and append subject metadata (Li et al., 30 Jul 2025).

Adhering to these guidelines, researchers can robustly elicit, measure, and control LLM personality profiles, enabling both transparent agent design and pluralistic user alignment.