Unveiling the Power of Retrieval-Augmented Generation (RAG) Models in Natural Language Processing
Natural Language Processing (NLP) has witnessed remarkable advancements over the years, with researchers continually pushing the boundaries of language model capabilities. One significant development in this domain is the emergence of Retrieval-Augmented Generation (RAG) models.
This article delves into the architecture, components, and applications of RAG models, exploring their evolution, advantages, and potential for transforming tasks such as question-answering and chatbot development.
Evolution of RAG Models
The genesis of RAG models can be traced back to the challenges faced by traditional Language Models (LLMs), which excelled in creative text generation but struggled with factual accuracy and context-specific responses. In response to these limitations, researchers sought ways to combine the strengths of both retrieval and generation techniques. In the 2020 research paper, the term Retrieval-Augmented Generation (RAG) was coined.
The concept of integrating retrieval and generation can be seen in earlier works, but the formalization of RAG models gained momentum in recent years. Notable contributions include the introduction of Dense Retrieval methods and advancements in Transformer…
