The growing need to represent diverse perspectives has increased interest in pluralistic LLM generation. Although difficult to operationalize, identifying perspectives expressed in text would provide clear guidance on pluralistic alignment and more clearly articulate the pluralistic gap in LLM generation. While models have been shown to reduce the diversity of training data and generate homogeneously, this has been demonstrated primarily on multiple-choice questionnaires or using high-level characteristics of free-form text. In this paper, we introduce and implement a domain-agnostic multi-layered framework for unsupervised extraction of perspectives suitable for identifying the pluralistic gap in LLM-generated text. We evaluate our framework on book reviews, a highly opinionated dataset representing diverse perspectives, and compare various prompts and models. Our results show that while some models and prompting techniques come close to covering a broad spectrum of perspectives, rarer perspectives remain disproportionately underrepresented, resulting in distributions that diverge from human text.

翻译：对多元视角的表征需求日益增长，使得多元主义大语言模型生成技术备受关注。尽管操作化难度较大，但识别文本中表达的视角可为多元对齐提供明确指导，并更清晰阐明大语言模型生成中的多元性缺陷。虽然已有研究表明模型会压缩训练数据多样性并生成同质化内容，但此类研究主要基于多项选择题或自由文本的高层特征展开。本文提出并实现了一种适用于无监督提取潜在视角的领域无关多层框架，可用于识别大语言模型生成文本中的多元性缺陷。我们以书评（一个高度观点化、代表多元视角的数据集）为评估基准，比较了不同提示词与模型的性能。研究结果表明，尽管部分模型及提示技术已接近覆盖广泛视角，但稀有视角仍系统性缺失，导致生成文本分布与人类文本存在显著差异。