Towards Latent Diffusion Suitable For Text

Fuente: arXiv
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Autori principali: Midavaine, Nesta, Naesseth, Christian A., Bartosh, Grigory
Natura: Preprint
Pubblicazione: 2026
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author Midavaine, Nesta
Naesseth, Christian A.
Bartosh, Grigory
author_facet Midavaine, Nesta
Naesseth, Christian A.
Bartosh, Grigory
contents Language diffusion models aim to improve sampling speed and coherence over autoregressive LLMs. We introduce Neural Flow Diffusion Models for language generation, an extension of NFDM that enables the straightforward application of continuous diffusion models to discrete state spaces. NFDM learns a multivariate forward process from the data, ensuring that the forward process and generative trajectory are a good fit for language modeling. Our model substantially reduces the likelihood gap with autoregressive models of the same size, while achieving sample quality comparable to that of previous latent diffusion models.
format Preprint
id arxiv_https___arxiv_org_abs_2601_16220
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Towards Latent Diffusion Suitable For Text
Midavaine, Nesta
Naesseth, Christian A.
Bartosh, Grigory
Computation and Language
Machine Learning
I.2.7
Language diffusion models aim to improve sampling speed and coherence over autoregressive LLMs. We introduce Neural Flow Diffusion Models for language generation, an extension of NFDM that enables the straightforward application of continuous diffusion models to discrete state spaces. NFDM learns a multivariate forward process from the data, ensuring that the forward process and generative trajectory are a good fit for language modeling. Our model substantially reduces the likelihood gap with autoregressive models of the same size, while achieving sample quality comparable to that of previous latent diffusion models.
title Towards Latent Diffusion Suitable For Text
topic Computation and Language
Machine Learning
I.2.7
url https://arxiv.org/abs/2601.16220