arXiv:2609.22607v1 Announce Type: new
Abstract: We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas,...
By Minwoo Kang, T\'ea Wright, Seun Eisape, Ayush Raj, Suhong Moon, Joseph Suh, Alane Suhr, David M. Chan, John Canny
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.
By Mengfan Li, Zesheng Wei, Xuanhua Shi, Yang Deng
arXiv:2604. 24079v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue.
By Jisoo Yang, Jongwon Ryu, Minuk Ma, Trung X. Pham, Junyeong Kim
arXiv:2609.39882v1 Announce Type: new
Abstract: Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-...
By Kemou Li, Zhuan Shi, Qizhou Wang, Fengpeng Li, Negar Rostamzadeh, Golnoosh Farnadi, Jiantao Zhou
arXiv:2604.17031v3 Announce Type: replace
Abstract: The individuation problem for large language models asks which entities associated with them, if any, should be identified as minds. We approach th...
By Pierre Beckmann, Patrick Butlin
arXiv:2609.22255v1 Announce Type: new
Abstract: Existing approaches to persona simulation with Large Language Models (LLMs) mostly rely on shallow character descriptions that fail to sustain coherent...
By Rotem Dror, Zohar Elyoseph, Yuval Haber, Elad Refoua, Oshrat Ayalon, Adir Solomon