Dec 19, 2023 · Autoregressive Encoder-Decoder Models: Nature: This hybrid approach integrates the sequential analysis capability of autoregressive models into both the encoding and decoding processes.

By training the encoder and decoder together, we have created what is known as a sequence-to-sequence model. If we train one part only, we get either an autoregressive or an autoencoding model. We'll cover each now.

Jun 17, 2023 · However, the encoder-only architectures, in contrast to decoder-only and encoder-decoder architectures, are not decoding in an autoregressive fashion. Autoregressive decoding refers to generating output sequences one token at a time, conditioning each token on the previously generated tokens.

Oct 6, 2024 · In this example, we will explore the encoder-decoder autoregressive architecture. The encoder-decoder model operates on a simple principle: the encoder processes a sequence of inputs (such...

For the decoder, autoregressive generation is used to produce an output sequence, an element at a time, until an end-of-sequence marker is generated. This incremen-

Oct 1, 2024 · These models (also called autoregressive models) use only the decoder stack, foregoing any encoders. Thus, when making token predictions, the model’s attention layers can only access those tokens preceding the token under consideration.

Decoder-based models (autoregressive models) are designed for text generation tasks. These models generate text one token at a time, using previously generated tokens as context for predicting the next token. Examples of decoder models include GPT (Generative Pretrained Transformer), GPT-2, and GPT-3.

Sep 8, 2023 · The encoder compresses the input data into a lower-dimensional representation, while the decoder reconstructs the input data from this compressed representation. Autoencoders are widely used for dimensionality reduction, denoising, and feature learning.

Sep 13, 2023 · Examples of encoder-only models: • BERT • ROBERTA. Decoder-Only Models: Decoder-models use only the decoder part of the transformer architecture. They are also known as autoregressive models. The objective of these models is to predict the next token, given the previous tokens.

Aug 23, 2023 · Understanding the distinctions between autoregressive and autoencoding language models sheds light on their unique mechanisms for text generation and representation.