分析83个生成用户画像的提示，发现多为简短结构化文本。
Using AI for User Representation: An Analysis of 83 Persona Prompts
- 用大模型生成用户画像时，多数使用单一人格描述。
- 近半数提示要求输出格式如JSON，74%含动态变量。
- 研究者常重复使用少量提示，跨模型比较罕见。
我们分析了27篇使用大语言模型（LLMs）生成用户画像的研究中包含的83个提示。结果表明，这些提示大多生成单一画像；许多提示要求简短描述，偏离了传统丰富、全面的画像风格。文本是最常见的属性格式，其次为数值。文本与数值常同时生成，几乎所有画像都包含人口统计属性。研究中最多使用12个提示，但多数仅用少量提示。跨模型比较和测试极为少见。超过一半的提示要求结构化输出（如JSON），74%的提示插入数据或动态变量。本文讨论计算化用户画像对用户表示的影响。
原文摘要 · Abstract (English)
We analyzed 83 persona prompts from 27 research articles that used large language models (LLMs) to generate user personas. Findings show that the prompts predominantly generate single personas. Several prompts express a desire for short or concise persona descriptions, which deviates from the tradition of creating rich, informative, and rounded persona profiles. Text is the most common format for generated persona attributes, followed by numbers. Text and numbers are often generated together, and demographic attributes are included in nearly all generated personas. Researchers use up to 12 prompts in a single study, though most research uses a small number of prompts. Comparison and testing multiple LLMs is rare. More than half of the prompts require the persona output in a structured format, such as JSON, and 74% of the prompts insert data or dynamic variables. We discuss the implications of increased use of computational personas for user representation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。