Publications (15)
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Keyon Vafa, Peter G. Chang, Ashesh Rambachan +1
Foundation models are premised on the idea that sequence prediction can uncover deeper domain understanding, much like how Kepler's predictions of planetary motion later led to the…
Evaluating the World Model Implicit in a Generative Model
Keyon Vafa, Justin Y. Chen, Ashesh Rambachan +2
Recent work suggests that large language models may implicitly learn world models. How should we assess this possibility? We formalize this question for the case where the underlyi…
Estimating Wage Disparities Using Foundation Models
Keyon Vafa, Susan Athey, David M. Blei
The rise of foundation models marks a paradigm shift in machine learning: instead of training specialized models from scratch, foundation models are first trained on massive datase…
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
Susan Athey, Herman Brunborg, Tianyu Du +2
This paper builds an empirical model that predicts a worker's next occupation as a function of the worker's occupational history. Because histories are sequences of occupations, th…
Text-Based Ideal Points
Keyon Vafa, Suresh Naidu, David M. Blei
Ideal point models analyze lawmakers' votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also g…
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
Keyon Vafa, Ashesh Rambachan, Sendhil Mullainathan
What makes large language models (LLMs) impressive is also what makes them hard to evaluate: their diversity of uses. To evaluate these models, we must understand the purposes they…