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Diffusion models in text generation: a survey.

Qiuhua Yi1, Xiangfan Chen1, Chenwei Zhang2

  • 1College of Computer Science and Technology, Zhejiang University of Technology, HangZhou, China.

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Summary
This summary is machine-generated.

Diffusion models, initially for images, now excel in natural language generation (NLG). This survey explores their use in text generation and compares them with pre-trained language models (PLMs).

Keywords:
Diffusion modelsNatural language generationText generation

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Diffusion models, originally for image generation, are gaining traction in Natural Language Processing (NLP).
  • Their ability to produce diverse and high-quality text outputs makes them a promising area for research.

Purpose of the Study:

  • To provide a comprehensive survey of diffusion models in text generation.
  • To compare diffusion models with autoregressive pre-trained language models (PLMs).
  • To identify challenges and future research directions for diffusion models in text generation.

Main Methods:

  • Categorizing text generation into conditional, unconstrained, and multi-mode.
  • Conducting a detailed comparison between diffusion models and PLMs across various dimensions.
  • Analyzing advantages, limitations, and potential integration strategies.

Main Results:

  • Diffusion models show significant potential for varied and high-quality text generation.
  • Comparison highlights distinct strengths and weaknesses of diffusion models versus PLMs.
  • Integration of PLMs into diffusion models is identified as a promising research avenue.

Conclusions:

  • Diffusion models offer a powerful alternative for text generation tasks.
  • Further research is needed to address challenges like sampling speed and explore multi-modal applications.
  • This survey serves as a reference for researchers and practitioners in the field.