Summary of A Survey on Rag Meeting Llms: Towards Retrieval-augmented Large Language Models, by Wenqi Fan et al.

Main: Computation and Language (cs.CL)
Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

by Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, Qing Li

First submitted to arxiv on: 10 May 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A comprehensive review of Retrieval-Augmented Large Language Models (RA-LLMs) is presented, highlighting the power of retrieval in providing up-to-date knowledge to augment language generation. Building on the advances of Large Language Models (LLMs), RA-LLMs harness external knowledge bases to improve generation quality and overcome limitations such as hallucinations and outdated internal knowledge. The survey covers three primary technical perspectives: architectures, training strategies, and applications, detailing challenges and capabilities of RA-LLMs.
Low	GrooveSquid.com (original content)	Low Difficulty Summary RA-LLMs use Retrieval-Augmented Generation (RAG) to provide reliable and up-to-date external knowledge, helping generative AI produce high-quality outputs. Recent research has shown the potential of RAG in improving LLMs’ language understanding and generation abilities. This survey reviews existing studies on RA-LLMs, discussing their architectures, training strategies, and applications.