Approximating Language Model Training Data from Weights

摘要

Modern language models often have open weights but closed training data. We formalize the problem of data recovery from model weights and propose several baselines and metrics. We develop a gradient-based approach that selects the highest-matching data from a large public text corpus and show its effectiveness at recovering data given only weights of the original and finetuned models. The training subset pinpointed by our method in a large corpus can be used to train another model to comparable performance. Even when none of the true training data is available, data selected by our method from publicly available Web documents can be used to train a competent model.