A Survey on RAG with LLMs
Laboratoire Interdisciplinaire Carnot de Bourgogne · National University of Computer and Emerging Sciences
Abstract
In the fast-paced realm of digital transformation, businesses are increasingly pressured to innovate and boost efficiency to remain competitive and foster growth. Large Language Models (LLMs) have emerged as game-changers across industries, revolutionizing various sectors by harnessing extensive text data to analyze and generate human-like text. Despite their impressive capabilities, LLMs often encounter challenges when dealing with domain-specific queries, potentially leading to inaccuracies in their outputs. In response, Retrieval-Augmented Generation (RAG) has emerged as a viable solution. By seamlessly integrating external data retrieval into text generation processes, RAG aims to enhance the accuracy and…
Citation impact
- FWCI
- 48.65
- Percentile
- 100%
- References
- 57
Authors
4Topics & keywords
- Computer science
- Data science
- Computer security
- Climate action