RAG: A Journey from Simple Query to Complex Narrative

Introduction

Retrieval Augmented Generation (RAG) is an advanced artificial intelligence (AI) technique that combines information retrieval with text generation, allowing Large Language Models (LLM) to retrieve relevant information from a knowledge source and incorporate it into AI generated text.

RAG framework fuses the strengths of pre-trained transformers and extractive question-answering systems. It provides a mechanism for integrating external knowledge into sequence generation models, thereby significantly enhancing their performance.

The Architecture of a RAG

A RAG operates in two primary stages: retrieval of documents pertinent to a given query, and generation of responses based on the retrieved documents and the query.?

• The retriever employs a dense vector space to rank documents according to their relevance to the query. This is achieved by transforming both the query and the documents into embeddings in a high-dimensional space, and then calculating the similarity between the query and each document.
• The generator, on the other hand, is a sequence-to-sequence model that crafts a response based on the query and the retrieved documents. The generator uses the embeddings of the retrieved documents and the query to generate a response.

The retriever and the generator are jointly fine-tuned during training, allowing the model to learn to retrieve documents that are most useful for generating accurate and relevant responses.

RAG and LLMs?

Large Language Models such as GPT, BERT, and Bard have demonstrated remarkable capabilities in generating human-like text. However, they often fall short in accessing and utilizing external knowledge.

This is where RAG steps in. By integrating a retriever into the model, RAG enables LLM to access a corpus of documents, thereby augmenting its knowledge base. This results in more accurate and informative responses. RAG technology ensures that LLMs generate responses based on reliable external data, rather than solely relying on their training data.

One way to think about RAG working with LLMs is a bit like hiring an intern from a top university. The university intern is likely to have a large amount of processing power, and very likely has a few areas of knowledge in which they are incredibly deep. However, like all other people, when they are thrown into a new contextual setting, they need some guidance to succeed.

Advantages of RAG?

RAG presents several advantages over traditional sequence generation models.

1. Firstly, it allows models to access external knowledge, thereby improving their performance
2. Secondly, it facilitates the fine-tuning of the retriever and the generator, thereby enhancing the relevance of the retrieved documents and the quality of the generated responses
3. Lastly, RAG models can be trained on a variety of tasks, making them highly versatile

The LLM is ordered to prioritize the external input data over its own generated response, ensuring that the answer is grounded in credible sources.

The Future of RAG?

The RAG framework signifies a substantial advancement in the field of natural language processing. By amalgamating the strengths of pre-trained transformers and extractive question-answering systems, RAG provides a potent tool for enhancing the performance of large language models. As research in this area progresses, we can anticipate the emergence of more sophisticated and powerful RAG models.

Future developments may include the integration of more advanced retrieval mechanisms, improved fine-tuning techniques, and the application of RAG to a wider range of GenAI tasks.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.        

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