Pretrained Language Models for Text Generation: A Survey

TL;DR

This survey reviews how pre-trained language models are utilized for text generation, covering encoding, model design, optimization, challenges, and future directions to guide researchers in the field.

Contribution

It provides a comprehensive overview of the core concepts, techniques, challenges, and future research directions in applying PLMs to text generation.

Findings

Identifies key aspects of applying PLMs to text generation

Summarizes challenges and potential solutions in the field

Highlights resources and applications of PLMs in text generation

Abstract

Text Generation aims to produce plausible and readable text in a human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. In this paper, we provide a survey on the utilization of PLMs in text generation. We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics which can be fused into PLMs; 2) how to design an effective PLM to serve as the generation model; and 3) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties. Then, we show the major challenges arisen in these aspects, as well as possible solutions for them. We also include a summary of various useful resources and typical text generation applications based on PLMs. Finally, we highlight the future research directions which will further improve these PLMs for text generation. This comprehensive survey is intended to help researchers interested in text generation problems to learn the core concepts, the main techniques and the latest developments in this area based on PLMs.