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Showing 1 to 3 of 3 for “"Text Infilling"”.

  1. Discrete Diffusion for Text Infilling

    Generative modeling of text is a fundamental challenge in natural language processing. While autoregressive models have achieved remarkable success, they face limitations in parallelizability and flexible control. Discrete diffusion models offer a promising alternative paradigm, leveraging …

    vt Repository record for Discrete Diffusion for Text Infilling (opens in a new tab)

  2. Generative Modeling with Guarantees

    … the accuracy and trustworthiness of the text generated by these models. In parallel, differential privacy has emerged as a framework to protect sensitive information while allowing machine learning algorithms to learn from it. Nevertheless, the trade-off between statistical guarantees and …

    mit Repository record for Generative Modeling with Guarantees (opens in a new tab)

  3. Controlling Neural Language Generation

    … and filling in blanks. Given partially specified text with one or more blanks, BLM will fill in the blanks with a variable number of tokens consistent with the context. Our model is well suited for a variety of text editing and rewriting tasks and demonstrates effectiveness on text infilling, …

    mit Repository record for Controlling Neural Language Generation (opens in a new tab)