arXiv:2510. 22170v3 Announce Type: replace Abstract: Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation.
arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.
arXiv:2607. 07916v1 Announce Type: new Abstract: Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them.
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:2606. 24162v1 Announce Type: cross Abstract: Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics.
arXiv:2606. 09843v3 Announce Type: replace-cross Abstract: Large language models (LLMs) give stable answers to personality questionnaires, yet these self-reports fail to predict how the models behave.
The paper introduces PRISM, a new framework for evaluating how well large language models (LLMs) maintain persona fidelity in dynamic dialogue. PRISM reframes the task as a structured inverse inference problem grounded in Systemic Functional Linguistics, breaking persona fidelity into Task Framing, Interpersonal Stance, and Linguistic Style dimensions. Experiments demonstrate that PRISM produces more accurate and stable judgments than existing holistic or static psychometric methods, offering a more reliable and auditable evaluation process.
arXiv:2510.13852v3 Announce Type: replace-cross Abstract: Is an LLM telling you different facts than it's telling me? This paper introduces ConsistencyAI, an independent benchmark for measuring the f...
The paper presents a systematic MBTI analysis of open‑source large language models (LLMs) across various quantization levels, including mainstream 4‑bit and extreme 2‑bit settings. It examines how personality traits emerge layer‑by‑layer through entropy and confidence‑gap dynamics, and introduces Uncertainty‑Amplified Layer Decoding (UALD) to study decoding‑induced personality drift. Findings show that personality is not static but depends on layer, quantization, prompting, and decoding, with ENFJ traits dominating, 4‑bit quantization preserving coarse structure, and 2‑bit quantization disrupting fine‑grained consistency.
arXiv:2606. 09843v1 Announce Type: cross Abstract: Large language models (LLMs) produce stable self-reports on personality inventories, but these self-reports do not predict observed behavior.
arXiv:2609.08592v1 Announce Type: new Abstract: Evaluating tool-augmented LLM agents requires diverse, realistic user inputs yet most evaluation frameworks use flat role descriptions ("you are an ang...
arXiv:2607. 08374v1 Announce Type: cross Abstract: Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological taxonomies rather than uncovering shared underlying behavioral structure.