We evaluate whether persona-based prompting improves Large Language Model (LLM) performance on macroeconomic forecasting tasks. Using 2,368 economics-related personas from the PersonaHub corpus, we prompt GPT-4o to replicate the ECB Survey of Professional Forecasters across 50 quarterly rounds (2013-2025). We compare the persona-prompted forecasts against the human experts panel, across four target variables (HICP, core HICP, GDP growth, unemployment) and four forecast horizons. We also compare the results against 100 baseline forecasts without persona descriptions to isolate its effect. We report two main findings. Firstly, GPT-4o and human forecasters achieve remarkably similar accuracy levels, with differences that are statistically significant yet practically modest. Our out-of-sample evaluation on 2024-2025 data demonstrates that GPT-4o can maintain competitive forecasting performance on unseen events, though with notable differences compared to the in-sample period. Secondly, our ablation experiment reveals no measurable forecasting advantage from persona descriptions, suggesting these prompt components can be omitted to reduce computational costs without sacrificing accuracy. Our results provide evidence that GPT-4o can achieve competitive forecasting accuracy even on out-of-sample macroeconomic events, if provided with relevant context data, while revealing that diverse prompts produce remarkably homogeneous forecasts compared to human panels.
When Persona Attributes Improve Population Alignment in Large Language Models
GESIS – Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；University of Mannheim（曼海姆大学）；Complexity Science Hub（复杂科学中心）；RWTH Aachen University（亚琛工业大学）
PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Institute of Data Science（数据科学研究所）；NUS Centre for Trusted Internet and Community, National University of Singapore（新加坡国立大学可信互联网与社区中心）；Amazon（亚马逊）；Stanford University（斯坦福大学）；Tsinghua University（清华大学）
Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models
Laboratoire de traitement et transport de l'information, Université Sorbonne Paris Nord, France（信息处理与传输实验室，索邦巴黎北大学，法国）；Institute of Communication Studies, National Yang Ming Chiao Tung University, Taiwan（传播学研究所，国立阳明交通大学，台湾）
Is ChatGPT a Good Personality Recognizer? A Preliminary Study
Institute of AI Education, East China Normal University（华东师范大学人工智能教育研究院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Psychology and Cognitive Science, East China Normal University（华东师范大学心理与认知科学学院）；Shanghai Changning Mental Health Center（上海市长宁区精神卫生中心）
German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies
University of Mannheim（曼海姆大学）；GESIS -- Leibniz Institute for the Social Sciences（莱布尼茨社会科学研究所）；RWTH Aachen University（亚琛工业大学）；Complexity Science Hub（复杂性科学中心）
Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Text
Computational Linguistics and Text Mining Lab, Vrije Universiteit, Amsterdam（自由大学计算语言学与文本挖掘实验室）；Università di Bologna（博洛尼亚大学）；CLCG, University of Groningen（格罗宁根大学CLCG）
When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；LG AI Research（LG人工智能研究院）；University of Illinois Chicago（芝加哥大学伊利诺伊分校）；University of Michigan（密歇根大学）
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
The Chinese University of Hong Kong（香港中文大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；National University of Singapore（新加坡国立大学）；Peng Cheng Laboratory（鹏城实验室）
PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health
Stony Brook University（石溪大学）；New York University（纽约大学）；University of Melbourne（墨尔本大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Pennsylvania（宾夕法尼亚大学）；Stanford University（斯坦福大学）
