An Overview of Diffusion Models for Text Generation
Abstract
Given the great success that diffusion models have achieved in generating various types of continuous data, including image, video and audio, there has been a growing interest in the application of these models to text generation. However, the discrete nature of text presents a challenge for diffusion models initially designed for application in a continuous feature space. The two main lines of work that aim to bring together diffusion models and natural language processing are focused on either defining the diffusion process in continuous space by converting discrete tokens to embeddings or defining the diffusion process in discrete space. These recent works attempt to combine diffusion models with leading sequence-to-sequence generation Transformer architecture as well as with existing pre-trained language models. In this work, we give a detailed overview of the approaches developed to date. We present and analyze the benefits and limitations that each model introduces, along with how they compare to the autoregressive models that dominate this field.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.