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arXiv AI

The Artificial Personality Model: Theory, Implementation, Measurement and Experimental Validation in Conversational Agents

The paper refines and empirically tests the Artificial Personality Model (APM), a hybrid framework that blends human‑centred and machine‑specific personality dimensions for conversational agents. Using three experiments with 395 participants, the authors operationalised the model with the Artificial Personality Questionnaire (APQ), finding that some human traits (e.g., agreeableness, conscientiousness) partially transfer while others (e.g., extraversion) do not, and identified new machine‑specific dimensions. The APQ showed promising psychometric properties, whereas the BFI‑2 performed poorly, and manipulations of artificial personality significantly affected user satisfaction, continuous use intention, and social presence.

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arXiv AI
Sep 21

Do Personality-Tuned LLMs Make Better Social Agents?

The paper examines whether fine‑tuning large language models (LLMs) with personality‑labelled data improves their ability to act as socially interactive agents. Two small open‑weight LLMs were fine‑tuned on a corpus of personality‑labelled social media posts and dialogues, and the resulting models were evaluated in various social interaction scenarios by independent LLM judges. The findings show that the fine‑tuned models do not outperform their baseline counterparts in role‑playing personalities, though they offer comparable text quality and increased linguistic diversity for the Qwen models; low inter‑rater agreement limits confidence in the results, suggesting future work should focus on training data quality and domain alignment.

By Tim Krabbe, Xiaodan Shi
llmsagentsroboticsfine-tuningsafety
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arXiv AI
Aug 19

Beyond BFI: The CSI for Enhanced Reliability and Validity in Evaluating LLM Personality Traits

The paper introduces the Core Sentiment Inventory (CSI), a new personality trait evaluation tool for large language models (LLMs) that addresses reliability and validity issues found in existing methods like the Big Five Inventory (BFI). CSI is designed specifically for LLMs, supports both English and Chinese, and provides detailed psychological portraits of model behavior. Experiments show that CSI captures nuanced behavioral patterns, improves reliability, and correlates strongly (above 0.85) with real-world LLM outputs.

By Huanhuan Ma, Haisong Gong, Xiaoyuan Yi, Xing Xie, Philip S. Yu, Dongkuan Xu
llms
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Hugging Face Trending Papers
Sep 8

A Three-Tier Persona Vector for Controllable User Simulation in Agentic Evaluation

The paper introduces a three-tier persona vector to generate diverse, realistic user inputs for evaluating tool-augmented LLM agents. The vector includes 23 dimensions: categorical demographics, continuous behavioral traits, and continuous emotional states, plus a query-complexity overlay. Experiments on 64,698 conversations show that these persona dimensions produce measurable differences in agent performance and realistic scenario-reactive behavior.

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arXiv Computation and Language
Oct 23

On the Behavioral Traits of LLM Agents

The paper introduces A-B-D, a method that infers personality traits of large language model agents from their behavioral data, combining functional actions and linguistic outputs across 345,667 real-world trajectories of 80 models. After filtering for stability and discriminability, 79 features are reduced to six reliable factors—two functional and four linguistic—revealing differences such as Claude‑Sonnet‑4.6’s planfulness versus GPT‑5.5’s low energy. The study also demonstrates a weak correlation between these inferred traits and agents’ self-reported Big Five scores, highlighting a knowledge‑action gap.

By Haokai Zhao, Jie Gao, Yunze Xiao, Xintao Wang, Weihao Xuan, Aditya Joshi, Mark Dredze, Jen-tse Huang
llmsagents
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arXiv AI
Sep 10

A Three-Tier Persona Vector for Controllable User Simulation in Agentic Evaluation

The paper introduces a three-tier persona vector for user simulation in evaluating LLM agents, comprising 23 dimensions across demographics, behavioral traits, and emotional states, plus a query-complexity overlay. It demonstrates that these nuanced personas generate diverse, scenario-reactive conversations, leading to significant variations in agent goal achievement and compliance across different contexts. The model’s design allows for reproducible, auditable user behavior patterns without relying on learned covariance matrices.

By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Kumar Das, Jitesh Chandra Mishra, Arun Menon, Avinash Karn, Mouli V
llmsagents
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