基于自我报告的LLM代理能够实现通用个体模拟
LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
AI总结：
本文研究了基于自我报告数据的LLM代理在模拟个体行为方面的有效性，通过不同数据源构建代理并验证其在多种任务中的准确性和跨群体公平性。
摘要：
机器学习在有大量结构化数据和明确结果的情况下可以准确预测人类行为，但这些模型通常局限于特定结果，难以应用到新领域。我们测试了大型语言模型（LLMs）是否能通过构建基于自我报告数据的个体特定模拟（即“生成代理”）来支持更通用的方法。使用来自1052名美国人的多样化样本数据，我们构建了代理，包括（i）两小时半结构化访谈（使用美国声音项目访谈计划获取）、（ii）结构化调查（一般社会调查和大五人格量表）或（iii）两者结合的数据。在保留的General Social Survey项目上，代理的准确率达到83%（仅访谈）、82%（仅调查）和86%（结合）的参与者两周测试再测一致性，相比仅使用个人人口统计数据的代理（74%）。代理在实验中预测人格特质和行为的准确性相似，且在种族和意识形态群体之间的准确性差异相对减少。这些结果表明，基于丰富定性或定量自我报告数据的LLM代理可以在不需任务特定训练数据的情况下，支持跨结果的个体通用模拟。
英文摘要：
Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes. Such models are typically outcome-specific, however, requiring training data for each target outcome, limiting their applicability to new domains. We test whether large language models (LLMs) can relax these requirements by using self-report data to build attitudinal and behavioral simulations, or "generative agents," that can predict responses across outcomes without outcome-specific training data. Using data from a diverse national sample of 1,052 Americans, we built agents from (i) two-hour, semi-structured interviews elicited using the American Voices Project interview schedule, (ii) structured surveys including General Social Survey items and the Big Five personality inventory, or (iii) both sources combined. On held-out General Social Survey items, interview-only, survey-only, and combined agents achieved accuracies equal to 83%, 82%, and 86% of participants' own two-week test-retest consistency benchmark, respectively, compared with 74% for demographics-only agents. Combining interviews and surveys produced the highest accuracy, though gains over either source alone were modest, suggesting that predictive benefits from data begin to asymptote once the model has observed sufficient evidence within a domain. We find that these agents also predict personality traits, economic-game behavior, and experimental responses, while reducing accuracy disparities across racial and ideological groups relative to demographics-only agents. Together, these results show that LLM agents grounded in qualitative or quantitative self-reports can support general-purpose simulation of individuals across outcomes, without requiring task-specific training data.
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