NLP

Introduction The “Attention is All You Need” paper by Vaswani et al. introduced the Transformer architecture, which has become a foundational model for many natural language processing (NLP) and other sequence-to-sequence tasks. Transformers are neural networks that learn context and understanding through sequential data analysis. The Transformer models use a modern and evolving mathematical techniques set, generally known as attention or self-attention. This self-attention mechanism lets each word in the sequence consider the entire context of the sentence, rather than just the words that came before it. This is akin to a person paying varying degrees of attention to different parts of a conversation. ...

Introduction The Retrieval-augmented Generation (RAG) framework combines the benefits of information retrieval systems with the generative capability of large language models. RAG is particularly useful in tasks that require a deep understanding of the query to generate contextually relevant responses. RAG workflow RAG involves two main components: a document retriever and a large language model (LLM). The retriever is responsible for finding relevant documents based on the input query and the generator uses the retrieved documents and the original query to generate a response. ...