Original
Using large language models to analyze political texts through natural language understanding
Abstract
Abstract Large language models (LLMs) offer scalable alternatives to human experts when analyzing political texts for meaning , using natural language understanding (NLU). Qualitative NLU methods relying on human experts are severely limited by cost and scalability. Statistical text‐as‐data methods are scalable but rely on strong and often unrealistic assumptions. We propose a systematic, scalable, and replicable method that can extend existing qualitative and quantitative approaches by using LLMs to interpret texts meaningfully rather than as mere data. Our ensemble means of LLM‐generated estimates of party positions on six key issue dimensions correlate highly with equivalent mean ratings by country specialists. When applied to coalition policy declarations, LLM estimates align more closely with standard models of government formation than hand‐coded estimates. We conclude with a discussion of the profound implications of modern LLMs for political text analysis.
中文
使用大型语言模型通过自然语言理解分析政治文本
摘要
大型语言模型（LLM）在利用自然语言理解（NLU）分析政治文本含义时，为人类专家提供了可扩展的替代方案。依赖人类专家的定性自然语言理解方法受到成本和可扩展性的严重限制。将文本作为数据的统计方法虽具可扩展性，但依赖于强有力且往往不现实的假设。我们提出一种系统、可扩展且可复制的方法，通过使用LLM对文本进行有意义地解读而非仅将其视为数据，来扩展现有的定性和定量方法。我们对六个关键议题维度上政党立场的LLM生成估计取集成均值，其与国别专家的等效平均评分高度相关。当应用于联盟政策宣言时，LLM估计比手工编码估计更接近标准的政府组建模型。最后，我们讨论现代LLM对政治文本分析的深远影响。
关键词
大型语言模型、自然语言理解、政治文本分析、政党立场、联盟政策宣言、政府组建、文本分析、研究方法