Recent Advances in Retrieval-Augmented Text Generation

Deng Cai, Yan Wang, Lemao Liu, Shuming Shi

Abstract

Recently, retrieval-augmented text generation has achieved state-of-the-art performance in many NLP tasks and has attracted increasing attention of the NLP and IR community, this tutorial thereby aims to present recent advances in retrieval-augmented text generation comprehensively and comparatively. It firstly highlights the generic paradigm of retrieval-augmented text generation, then reviews notable works for different text generation tasks including dialogue generation, machine translation, and other generation tasks, and finally points out some limitations and shortcomings to facilitate future research.

Slides

Reference

Tutorial Proposal
A Survey on Retrieval-Augmented Text Generation
A Reading List for Retrieval-augmented Text Generation