Today's probabilistic language generators fall short when it comes to producing coherent
and fluent text despite the fact that the underlying models perform well under standard …

We propose an alternate approach to quantifying how well language models learn natural
language: we ask how well they match the statistical tendencies of natural language. To …

Large pretrained language models generate fluent text but are notoriously hard to
controllably sample from. In this work, we study constrained sampling from such language …

This paper shows that full abstraction can be accomplished in the context of guided
summarization. We describe a work in progress that relies on Information Extraction …

This work studies discrete diffusion probabilistic models with applications to natural
language generation. We derive an alternative yet equivalent formulation of the sampling …

To what degree can language be acquired from linguistic input alone? This question has
vexed scholars for millennia and is still a major focus of debate in the cognitive science of …

Long samples of text from neural language models can be of poor quality. Truncation
sampling algorithms--like top-$ p $ or top-$ k $--address this by setting some words' …

Most previous work on trainable language generation has focused on two paradigms:(a)
using a statistical model to rank a set of generated utterances, or (b) using statistics to inform …

When generating text from probabilistic models, the chosen decoding strategy has a
profound effect on the resulting text. Yet the properties elicited by various decoding …

A goal of statistical language modeling is to learn the joint probability function of sequences
of words. This is intrinsically difficult because of the curse of dimensionality: we propose to …