A Survey on Retrieval-Augmented Text Generation for Large Language Models

Retrieval-Augmented Generation (RAG) merges information retrieval (IR) techniques with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but possibly incorrect responses by LLMs, thereby enhancing the accuracy and reliability of their outputs through the use of real-world data. As RAG grows in complexity and incorporates multiple concepts that can influence its performance, this article organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval,…

• NS
  Natural Sciences and Engineering Research Council of Canada