What is (RAG) retrieval augmented generation?

Retrieval Augmented Generation (RAG) is a type of natural language processing (NLP) model that combines the strengths of retrieval-based and generation-based models. In traditional generation-based models, the model generates text based on patterns learned from the training data. In contrast, retrieval-based models retrieve relevant text from a database or knowledge graph to answer a query or generate text.

RAG models leverage both approaches by first retrieving relevant texts or passages from a database and then using a generation model to generate the final output text. This allows RAG models to take advantage of the strength of retrieval models in providing factual accuracy and the strength of generation models in generating fluent and coherent text.

RAG models have been shown to be effective in various NLP tasks, including question answering, text summarization, and dialogue generation. They are particularly useful when the task requires generating text that is grounded in specific knowledge or facts.

Some popular examples of RAG models include the RAG-token and RAG-seq models, which have achieved state-of-the-art results in various NLP benchmarks.

If you're interested in exploring RAG models further, I recommend checking out the following resources:

• The RAG-model paper by Lewis et al. (2020)
• The Hugging Face Transformers library, which provides implementations of RAG models
• The Stanford Natural Language Processing Group's resources on RAG models