Towards Latent Diffusion Suitable For Text

• URL: http://arxiv.org/abs/2601.16220v1
• Date: Wed, 07 Jan 2026 20:50:59 GMT
• Title: Towards Latent Diffusion Suitable For Text
• Authors: Nesta Midavaine, Christian A. Naesseth, Grigory Bartosh,
• Abstract summary: 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.<n>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.
• Score: 7.293508593001522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• One-step Language Modeling via Continuous Denoising [36.18484491074519]
  We show that language models leveraging flow-based continuous denoising can outperform discrete diffusion in both quality and speed.<n>Our work calls into question the widely held hypothesis that discrete diffusion processes are necessary for generative modeling over discrete modalities.
  arXiv  Detail & Related papers  (2026-02-18T19:23:07Z)
• Continuous Diffusion Model for Language Modeling [64.7425225935854]
  Existing continuous diffusion models for discrete data underperform compared to discrete methods.<n>We propose a continuous diffusion model for language modeling that incorporates the geometry of the underlying categorical distribution.<n>Our method outperforms existing discrete diffusion models and approaches the performance of autoregressive models.
  arXiv  Detail & Related papers  (2025-02-17T08:54:29Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step.<n>Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Scaling Diffusion Language Models via Adaptation from Autoregressive Models [105.70889434492143]
  Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling.<n>We show that we can convert AR models ranging from 127M to 7B parameters into diffusion models DiffuGPT and DiffuLLaMA, using less than 200B tokens for training.<n>Our experimental results reveal that these models outperform earlier DLMs and are competitive with their AR counterparts.
  arXiv  Detail & Related papers  (2024-10-23T14:04:22Z)
• LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Models [55.5427001668863]
  We present a novel latent diffusion model dubbed LDMol for text-conditioned molecule generation.<n> Experiments show that LDMol outperforms the existing autoregressive baselines on the text-to-molecule generation benchmark.<n>We show that LDMol can be applied to downstream tasks such as molecule-to-text retrieval and text-guided molecule editing.
  arXiv  Detail & Related papers  (2024-05-28T04:59:13Z)
• Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling [2.1779479916071067]
  We introduce a novel framework that enhances diffusion models by supporting a broader range of forward processes.<n>We also propose a novel parameterization technique for learning the forward process.<n>Results underscore NFDM's versatility and its potential for a wide range of applications.
  arXiv  Detail & Related papers  (2024-04-19T15:10:54Z)
• Likelihood-Based Diffusion Language Models [13.916640262862215]
  We take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models. We pursue this goal through algorithmic improvements, scaling laws, and increased compute. We release Plaid 1B, a large diffusion language model which outperforms GPT-2 124M in likelihood on benchmark datasets.
  arXiv  Detail & Related papers  (2023-05-30T16:43:31Z)
• A Survey of Diffusion Models in Natural Language Processing [11.233768932957771]
  Diffusion models capture the diffusion of information or signals across a network or manifold. This paper discusses the different formulations of diffusion models used in NLP, their strengths and limitations, and their applications.
  arXiv  Detail & Related papers  (2023-05-24T03:25:32Z)
• A Cheaper and Better Diffusion Language Model with Soft-Masked Noise [62.719656543880596]
  Masked-Diffuse LM is a novel diffusion model for language modeling, inspired by linguistic features in languages. Specifically, we design a linguistic-informed forward process which adds corruptions to the text through strategically soft-masking to better noise the textual data. We demonstrate that our Masked-Diffuse LM can achieve better generation quality than the state-of-the-art diffusion models with better efficiency.
  arXiv  Detail & Related papers  (2023-04-10T17:58:42Z)
• A Reparameterized Discrete Diffusion Model for Text Generation [39.0145272152805]
  This work studies discrete diffusion probabilistic models with applications to natural language generation. We derive an alternative yet equivalent formulation of the sampling from discrete diffusion processes. We conduct extensive experiments to evaluate the text generation capability of our model, demonstrating significant improvements over existing diffusion models.
  arXiv  Detail & Related papers  (2023-02-11T16:26:57Z)
• DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models [81.84866217721361]
  DiffusionBERT is a new generative masked language model based on discrete diffusion models. We propose a new noise schedule for the forward diffusion process that controls the degree of noise added at each step. Experiments on unconditional text generation demonstrate that DiffusionBERT achieves significant improvement over existing diffusion models for text.
  arXiv  Detail & Related papers  (2022-11-28T03:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.