Papers by Volodymyr Kuleshov

2 papers
Text Embeddings Reveal (Almost) As Much As Text (2023.emnlp-main)

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Challenge: a vector database of dense text embeddings stores only the text data, not the original text . a multi-step method that iteratively corrects and re-embeds text can recover 92% of 32-token text inputs exactly.
Approach: They propose a method that iteratively corrects and re-embeds text to recover 92% of 32-token text inputs exactly.
Outcome: The proposed method recovers 92% of 32-token text inputs exactly.
Model Criticism for Long-Form Text Generation (2022.emnlp-main)

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Challenge: Language models generate fluent text, but it remains unclear whether output retains coherent high-level structure.
Approach: They propose to use a statistical tool to evaluate high-level structure of text . they compare distributions between real and generated data in latent space .
Outcome: The proposed model criticism compares distributions between real and generated data in a latent space . different generative processes identify specific failure modes of the underlying model .

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