A Survey on Retrieval-Augmented Text Generation

Understand

Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community.

• Compared with conventional generation models, retrieval-augmented text generation has remarkable advantages and particularly has achieved state-of-the-art performance in many NLP tasks.
• This paper aims to conduct a survey about retrieval-augmented text generation.
• It firstly highlights the generic paradigm of retrieval-augmented generation, and then it reviews notable approaches according to different tasks including dialogue response generation, machine translation, and other generation tasks.