An Introduction to Retrieval Augmented Generation (RAG)

Retrieval augmented generation (RAG) is an approach in natural language processing that combines a neural retriever with a neural generator to produce coherent and factual text. RAG models have shown promising results in tasks like open-ended question answering, summarization, and dialogue.

How RAG Models Work

RAG models consist of two main components:

Retriever: This is responsible for retrieving relevant context documents or passages of text from a large corpus or database given a query or prompt. The retriever ranks and returns the most relevant texts. Popular retrievers include dense retrievers based on bi-encoders and sparse retrievers based on inverted indexes.

Generator: This takes the query and retrieved documents as input and generates an output text. The generator is usually a pre-trained language model like GPT-3. By conditioning the generation on relevant texts, the generator can produce more factual, specific, and coherent outputs.

During inference, the retriever first retrieves relevant contexts for the given query or prompt. The generator then conditions on these contexts as well as the original query to generate the final output. The retriever and generator are trained jointly, often with a Generative QA (GenQA) objective of maximizing the likelihood of generating the ground truth text.

Benefits of RAG Models

Some key benefits of RAG models include:

Knowledgeable: By retrieving relevant information, RAG models can incorporate facts and knowledge into generated text. This makes them better at factual correctness.

Specific: Retrieved texts provide useful cues to generate more focused and specific outputs.

Coherent: The generator can draw connections between the retrieved contexts and query to produce holistic coherent text.

Scalable training: RAG models provide a more sample efficient way to inject knowledge into generators compared to having to train end-to-end on massive datasets.

RAG Applications

RAG models have shown promising results on several NLP tasks:

Question answering: RAG can answer open-ended questions by retrieving related passages and generating from them.

Summarization: RAG can summarize long documents by retrieving salient passages.

Dialogue: RAG can make chatbots and assistants more knowledgeable by retrieving useful context.

Multi-hop reasoning: RAG can do complex inference by chaining and reasoning over multiple retrieved passages.

Overall, RAG offers an effective way to make NLG models more factual, specific, and knowledgeable. As retrieval indexes and pre-trained models continue to improve, we can expect further advances in RAG’s capabilities.