Stephen Qingyuan Yang

"Unstructured textual information is important for understanding innovation but challenging to study. The advent of large-language models (LLMs) relieves this constraint. This paper develops deep-learning predictive models incorporating ChatGPT textual embeddings to access intricate information about patent quality."

"We combine advanced language models with broadly applicable perception tests to overcome restrictive assumptions in "causal" machine learning and identify high-dimensional discrimination using unstructured data."

"We develop a model of collective choice that introduces utility to individual voting and study polarized, partisan legislatures. Party allegiances bring about lower social welfare via inefficient policy selection."