Build a Large Language Model
Metadata
Highlights
visual representation of the transformer’s encoder and decoder submodules. On the left, the encoder segment exemplifies BERT-like LLMs, which focus on masked word prediction and are primarily used for tasks like text classification. On the right, the decoder segment showcases GPT-like LLMs, designed for generative tasks and producing coherent text sequences. — location: 141
LLMs have transformed the field of natural language processing, which previouslyrelied on explicit rule-based systems and simpler statistical methods. The advent of LLMs introduced new deep learning-driven approaches that led to advancements in understanding, generating, and translating human language. Modern LLMs are trained in two main steps. First, they are pretrained on a large corpus of unlabeled text by using the prediction of the next word in a sentence as a “label.” Then, they are finetuned on a smaller, labeled target dataset to follow instructions or perform classification tasks. LLMs are based on the transformer architecture. The key idea of the transformer architecture is an attention mechanism that gives the LLM selective access to the whole input sequence when generating the output one word at a time. The original transformer architecture consists of an encoder for parsing text and a decoder for generating text. — location: 267