Jun 17, 2023 · Delve into Transformer architectures: from the original encoder-decoder structure, to BERT & RoBERTa encoder-only models, to the GPT series focused on decoding. Explore their evolution, strengths, & applications in NLP tasks.

Feb 3, 2024 · In a translation task, an encoder takes an English sentence and converts it into a vector that represents its linguistic features and meaning. Decoder: Takes the encoded representation and...

Sep 22, 2024 · Encoder-Only vs Decoder-Only Style LLM Architectures: Understanding the Differences. Language models have transformed the field of natural language processing, with encoder-only and decoder-only architectures playing crucial roles.

Apr 8, 2023 · This paper aims to address this gap by conducting a detailed comparison between the encoder-decoder architecture and the decoder-only language model framework through the analysis of a regularized encoder-decoder structure.

Mar 22, 2024 · What are Encoders in LLMs? Encoders in the context of LLMs are algorithmic structures designed to process and transform input text into a format that the model can understand and manipulate.

May 4, 2024 · Some others, like T5, have both an encoder and a decoder, with some small modifications on the architecture and training strategy. Why some LLMs took only a part of the original transformer...

Apr 27, 2024 · In this comprehensive guide, we will explore the inner workings of decoder-based LLMs, delving into the fundamental building blocks, architectural innovations, and implementation details that have propelled these models to the forefront of NLP research and applications.

Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend.

Gain a comprehensive understanding of LLM architecture; Construct and deploy real-world applications using LLMs; Learn the fundamentals of search and retrieval for AI applications; Understand encoder and decoder models at a deep level; Train, fine-tune, and deploy LLMs for enterprise use cases; Implement RAG-based architectures with open-source ...

Encoder - accepts the input data and converts it into an abstract continuous representation that captures the main characteristics of the input. Decoder - translates the continuous representation into intelligible outputs while ingesting its previous outputs.