Challenge: Self-report questionnaires are used to assess LLM personality traits, but they fail to capture behavioral nuances due to biases and meta-knowledge contamination.
Approach: They propose a multi-observer framework for personality trait assessments in LLM agents that draws on informant-report methods in psychology.
Outcome: The proposed framework combines multiple observers with a subject LLM agent to assess its Big Five personality traits.

Similar Papers

Modeling, Evaluating, and Embodying Personality in LLMs: A Survey (2025.findings-emnlp)

Copied to clipboard

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

Copied to clipboard

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.
Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)

Copied to clipboard

Challenge: Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks.
Approach: They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona.
Outcome: The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents .
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics (2025.findings-naacl)

Copied to clipboard

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.
Can LLM Agents Maintain a Persona in Discourse? (2025.emnlp-main)

Copied to clipboard

Challenge: Large language models are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions.
Approach: They propose to use two conversation agents to generate a discourse with an assigned personality from the OCEAN framework and then use multiple judge agents to infer original traits.
Outcome: The proposed model is based on two conversation agents with a personality assigned from the OCEAN framework and then multiple judge agents to infer the original traits assigned.
Can ChatGPT Assess Human Personalities? A General Evaluation Framework (2023.findings-emnlp)

Copied to clipboard

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.
PersonaGym: Evaluating Persona Agents and LLMs (2025.findings-emnlp)

Copied to clipboard

Challenge: Persona agents are LLM agents conditioned to act according to an assigned persona . evaluating how faithfully these agents adhere to their personas remains a challenge .
Approach: a new study evaluates persona agents' ability to act according to an assigned persona . a persona agent's person score is a human-aligned automatic metric that can be used to evaluate a model .
Outcome: a new evaluation framework and a human-aligned automatic metric show that persona agents can perform better.
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization (2024.findings-emnlp)

Copied to clipboard

Challenge: Existing literature on leveraging persona in large language models is disorganized and lacks a systematic taxonomy . leveraging peopleas has resurfaced as an ideal lens for adapting LLMs for specific contexts .
Approach: They propose to categorize current research on leveraging persona in large language models . they propose to use a comprehensive survey to categorize existing studies .
Outcome: The proposed framework is a promising framework for tailoring large language models to specific contexts.
PADO: Personality-induced multi-Agents for Detecting OCEAN in human-generated texts (2025.coling-main)

Copied to clipboard

Challenge: Existing methods for personality detection are limited due to the latent and relative nature of personality and lack of annotated datasets.
Approach: They propose a method that exploits the inherent knowledge of Large Language Models to capture the relative nature of personality traits by comparing contrasting perspectives.
Outcome: The proposed approach exploits the inherent knowledge of Large Language Models to capture the relative nature of personality traits.
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)

Copied to clipboard

Challenge: LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored .
Approach: They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate .
Outcome: The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations