超越边缘分布：评估与文化价值观对齐的大型语言模型代表性的框架
Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs
AI总结：
本文提出评估对齐模型代表性的框架，通过多变量相关性模式和边缘分布相结合，比较两种模型引导技术，发现尽管细调模型更接近边缘分布，但提示方法在再现调查项目相关性结构上更优，但两者均未与人类相关性模式一致。
摘要：
大型语言模型越来越多地用于代表人类观点、价值观或信念，其向这些理想方向的可引导性是研究的活跃领域。现有工作主要集中在对齐边缘响应分布上，将每个对齐评估示例独立对待。虽然必要，但可能忽视了刻画真实人口的深层潜在结构和支撑文化价值观理论的基础。我们提出一个框架，通过多变量相关性模式以及边缘分布来评估对齐模型的代表性。我们通过比较两种模型引导技术（人物提示和人口细调）并评估它们与世界价值观调查的人类响应进行比较，展示了我们评估方案的价值。尽管人口细调模型更好地近似边缘响应分布，人物提示在再现调查项目之间的经验相关性结构上略优。尽管存在这种反转，这两种技术均未与人类相关性模式一致。我们得出结论，代表性是价值对齐的一个独特方面，专注于边缘的评估可能掩盖结构性失败，导致对模型代表性的过于乐观结论。
英文摘要：
Large language models are increasingly used to represent human opinions, values, or beliefs, and their steerability towards these ideals is an active area of research. Existing work focuses predominantly on aligning marginal response distributions, treating each alignment evaluation example independently. While essential, this may overlook deeper latent structures that characterise real populations and underpin cultural values theories. We propose a framework for evaluating the \textit{representativeness} of aligned models through multivariate correlation patterns in addition to marginal distributions. We show the value of our evaluation scheme by comparing two model steering techniques (persona prompting and demographic fine-tuning) and evaluating them against human responses from the World Values Survey. While the demographic fine-tuned model better approximates marginal response distributions, persona prompting performs marginally better at reproducing the empirical correlation structure between survey items. Despite this reversal, neither technique aligns with human correlation patterns. We conclude that representativeness is a distinct aspect of value alignment and an evaluation focused on marginals can mask structural failures, leading to overly optimistic conclusions about model representativeness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
