Challenge: Large Language Models (LLMs) are increasingly integrated into everyday workflows . a recent study found that LLMs exhibit distinct personality-like traits that affect user engagement .
Approach: They evaluated 32 LLM users for four collaborative tasks and found significant preferences . they found that rationalists preferred GPT-4, while idealists favored Claude 3.5 .
Outcome: The results show that users with different personality traits prefer certain LLMs over others.

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PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits (2024.findings-naacl)

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Challenge: Recent studies have shown that LLMs can generate content that aligns with their assigned personality traits, but there is limited research on whether they consistently reflect specific personality traits.
Approach: They propose to study the behavior of LLM-based agents which they refer to as LLM personas and simulate them to measure their personality traits.
Outcome: The proposed model is based on the Big Five personality model and has been validated by human evaluations and automatic evaluations.
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics (2025.findings-naacl)

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Challenge: Recent advances in Large Language Models (LLMs) have led to their adaptation as conversational agents.
Approach: They propose a new benchmark that uses 8K multi-choice questions to assess the personality of Large Language Models.
Outcome: The proposed personality test outperforms existing personality tests for LLMs in reliability and validity.
Modeling, Evaluating, and Embodying Personality in LLMs: A Survey (2025.findings-emnlp)

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Challenge: This survey provides a comprehensive overview of the LLM-driven personality scenario.
Approach: This survey provides a comprehensive overview of the LLM-driven personality scenario.
Outcome: The proposed taxonomy analyzes the limitations of existing methods and identifies key research gaps.
Exploring the Impact of Language Switching on Personality Traits in LLMs (2025.coling-main)

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Challenge: Using three personality tests, we examine the extent to which LLMs align with humans when personality shifts are associated with language changes.
Approach: They propose to use the Eysenck Personality Questionnaire-Revised to examine whether LLMs align with humans when personality shifts are associated with language changes.
Outcome: The results show that language-switching affects personality traits in multilingual individuals, and that it is not translation-related.
Dissecting Human and LLM Preferences (2024.acl-long)

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Challenge: a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation.
Approach: They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition.
Outcome: The proposed model is compared with 32 different large language models using real-world user-model conversations.
Manipulating the Perceived Personality Traits of Language Models (2023.findings-emnlp)

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Challenge: Psychology research has long explored aspects of human personality like extroversion, agreeableness and emotional stability, three of the personality traits that make up the ‘Big Five’.
Approach: They propose to use text generated from large language models to evaluate perceived personality traits and to frame them as tools for controlling personas in dialog systems.
Outcome: The proposed models predict personality traits in different contexts and can be manipulated in a predictable way.
One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization (2026.acl-long)

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Challenge: Prior work has used personas to study biases by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions.
Approach: They compare six commonly used personacues across seven open and proprietary LLMs on four writing and advice tasks.
Outcome: The proposed model is based on a persona, a synthetic user profile defined by specific attributes, defined by gender or race.
Exploring the Impact of Personality Traits on LLM Toxicity and Bias (2025.emnlp-main)

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Challenge: anthropomorphic LLMs are being developed to serve diversified roles, but content safety concerns remain regarding their toxicity and toxicity.
Approach: They propose to assign personality traits to large language models (LLMs) to reduce toxic language and social biases in their outputs by using the widely accepted HEXACO personality framework developed in social psychology.
Outcome: The proposed model is able to perform on three toxic and bias benchmarks and shows that assigning personality traits reduces bias and toxicity similar to humans’ correlations between personality traits and toxic behaviors.
Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses (2025.findings-emnlp)

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Challenge: large language models (LLMs) increasingly assist subjective decision-making . prior work uses aggregate human judgments, but demographic variation and its linguistic drivers remain underexplored.
Approach: They analyze how demographic background and empathy level correlate with LLM-generated dilemma responses . they also identify markers that predict group-level differences .
Outcome: The authors show that demographic background and empathy level correlate with LLM preferences . their findings highlight the need for demographically informed LLM evaluations.
Can ChatGPT Assess Human Personalities? A General Evaluation Framework (2023.findings-emnlp)

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Challenge: Existing studies study the virtual personalities of LLMs but rarely explore the possibility of analyzing human personalities via LLM.
Approach: They propose to use Myers–Briggs Type Indicator (MBTI) tests to generate unbiased prompts and replace the subject in question statements to enable flexible queries and assessments.
Outcome: The proposed framework enables LLMs to flexibly assess personalities of different groups of people.

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