Language modeling via stochastic processes

Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori Hashimoto

Keywords: contrastive learning, language modelling, stochastic processes

Abstract: Modern language models can generate high-quality short texts. However, they often meander or are incoherent when generating longer texts. These issues arise from the next-token-only language modeling objective. To address these issues, we introduce Time Control (TC), a language model that implicitly plans via a latent stochastic process. TC does this by learning a representation which maps the dynamics of how text changes in a document to the dynamics of a stochastic process of interest. Using this representation, the language model can generate text by first implicitly generating a document plan via a stochastic process, and then generating text that is consistent with this latent plan. Compared to domain-specific methods and fine-tuning GPT2 across a variety of text domains, TC improves performance on text infilling and discourse coherence. On long text generation settings, TC preserves the text structure both in terms of ordering (up to +40% better) and text length consistency (up to +17% better). Human evaluators also prefer TC's output 28.6% more than the baselines.

One-sentence Summary: We introduce a language model that implicitly plans via a latent stochastic process.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2203.11370/code)