Pretrained Language Model for Text Generation: A Survey
Pretrained Language Model for Text Generation: A Survey
Junyi Li,Tianyi Tang,Wayne Xin Zhao,Ji-rong Wen
TLDR
This paper presents an overview of the major advances achieved in the topic of pretrained language models for text generation and discusses how to adapt existing PLMs to model different input data and satisfy special properties in the generated text.
Abstract
Text generation has become one of the most important yet challenging tasks in natural language processing (NLP). The resurgence of deep learning
has greatly advanced this field by neural generation models, especially the paradigm of pretrained language models (PLMs). In this paper, we present
an overview of the major advances achieved in the topic of PLMs for text generation. As the preliminaries, we present the general task definition
and briefly describe the mainstream architectures of PLMs for text generation. As the core content, we discuss how to adapt existing PLMs to model
different input data and satisfy special properties in the generated text. We further summarize several important fine-tuning strategies for text generation.
Finally, we present several future directions and conclude this paper. Our survey aims to provide text generation researchers a synthesis and pointer to related research.