arXiv AI
Evaluating the Efficacy of LLMs to Emulate Realistic Human Personalities
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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.
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.
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.
arXiv AI
Sep 4
Human Psychometric Questionnaires Mischaracterize LLM Behavior
The paper investigates whether human psychometric questionnaires can reliably characterize large language models (LLMs) in everyday interactions. By comparing eight open‑source LLMs’ value and personality profiles from Likert self‑reports (PVQ‑40/21 and BFI‑44/10) with generation probabilities on value‑laden user queries, the authors find substantial divergence between the two methods. The study shows that questionnaire items contain explicit lexical cues that lead models to respond in socially desirable ways, whereas realistic user queries lack such cues, and demographic persona prompts shift questionnaire responses but not generation outputs, indicating that questionnaire scores overestimate LLMs’ true behavioral tendencies.
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.
arXiv Computation and Language
Sep 23
PERSONAWEAVER: Controllable Diversity Beyond Conventional Archetypes in Procedural Character Generation
PERSONAWEAVER is a new approach to procedural character generation that separates world building from behavioral specification, using manually curated banks of moral positions and conversational reactions to diversify character behavior. By applying this method across ten realistic and fantastical settings and three large language models, the system produces broader moral and interactional response distributions, varied interpersonal language, response length, sentiment, and less archetypal world attribute combinations compared to prior work.