31 citations · 41 across the 5 of their papers we have counts for
6 papers
Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches
Patrick Y. Wu
After a disruptive event or shock, such as the Department of Government Efficiency (DOGE) federal layoffs of 2025, expert judgments are colored by knowledge of the outcome. This ca…
Evaluating how LLM annotations represent diverse views on contentious topics
Megan A. Brown, Shubham Atreja, Libby Hemphill +1
Researchers have proposed the use of generative large language models (LLMs) to label data for research and applied settings. This literature emphasizes the improved performance of…
Concept-Guided Chain-of-Thought Prompting for Pairwise Comparison Scoring of Texts with Large Language Models
Patrick Y. Wu, Jonathan Nagler, Joshua A. Tucker +1
Existing text scoring methods require a large corpus, struggle with short texts, or require hand-labeled data. We develop a text scoring framework that leverages generative large l…
Large Language Models Can Be Used to Estimate the Latent Positions of Politicians
Patrick Y. Wu, Jonathan Nagler, Joshua A. Tucker +1
Existing approaches to estimating politicians' latent positions along specific dimensions often fail when relevant data is limited. We leverage the embedded knowledge in generative…
Dictionary-Assisted Supervised Contrastive Learning
Patrick Y. Wu, Richard Bonneau, Joshua A. Tucker +1
Text analysis in the social sciences often involves using specialized dictionaries to reason with abstract concepts, such as perceptions about the economy or abuse on social media.…
MARMOT: A Deep Learning Framework for Constructing Multimodal Representations for Vision-and-Language Tasks
Patrick Y. Wu, Walter R. Mebane
Political activity on social media presents a data-rich window into political behavior, but the vast amount of data means that almost all content analyses of social media require a…