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Found 10 papers, 8 papers with code
Estimating Wage Disparities Using Foundation Models
Classical methods for decomposing the wage gap employ simple predictive models of wages which condition on a small set of simple summaries of labor history.
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
For the task of next job prediction, we demonstrate that models trained with our approach outperform several alternatives in terms of predictive performance on the survey data, including traditional econometric models, CAREER, and LLMs with in-context learning, even though the LLM can in principle predict job titles that are not allowed in the survey data.
Evaluating the World Model Implicit in a Generative Model
Building generative models that meaningfully capture the underlying logic of the domains they model would be immensely valuable; our results suggest new ways to assess how close a given model is to that goal.
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
Our results show that -- especially for cases where the cost of mistakes is high -- more capable models (e. g. GPT-4) can do worse on the instances people choose to use them for, exactly because they are not aligned with the human generalization function.
Revisiting Topic-Guided Language Models
A recent line of work in natural language processing has aimed to combine language models and topic models.
An Invariant Learning Characterization of Controlled Text Generation
In this paper, we show that the performance of controlled generation may be poor if the distributions of text in response to user prompts differ from the distribution the predictor was trained on.
CAREER: A Foundation Model for Labor Sequence Data
We fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it on small longitudinal survey datasets.
Rationales for Sequential Predictions
Compared to existing baselines, greedy rationalization is best at optimizing the combinatorial objective and provides the most faithful rationales.
Text-Based Ideal Points
In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors.
Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown.
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