4 citations · 16 across the 11 of their papers we have counts for
4 papers · 1 filter
Approximating Language Model Training Data from Weights
John X. Morris, Junjie Oscar Yin, Woojeong Kim +2
Modern language models often have open weights but closed training data. We formalize the problem of data approximation from model weights and propose several baselines and metrics…
How much do language models memorize?
John X. Morris, Chawin Sitawarin, Chuan Guo +5
We propose a new method for estimating how much a model knows about a datapoint and use it to measure the capacity of modern language models. Prior studies of language model memori…
Harnessing the Universal Geometry of Embeddings
Rishi Jha, Collin Zhang, Vitaly Shmatikov +1
We introduce the first method for translating text embeddings from one vector space to another without any paired data, encoders, or predefined sets of matches. Our unsupervised ap…
Universal Zero-shot Embedding Inversion
Collin Zhang, John X. Morris, Vitaly Shmatikov
Embedding inversion, i.e., reconstructing text given its embedding and black-box access to the embedding encoder, is a fundamental problem in both NLP and security. From the NLP pe…