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Jun 18, 2019 · The same section of the paper describes encoder-decoder as follows: Encoder-Decoder models are a family of models which learn to map data-points from an input domain to an output domain via a two-stage network: The encoder, represented by an encoding function z = f(x), compresses the input into a latent-space representation; the decoder, y = g(z), aims to predict the output from the latent space representation.
Nov 16, 2020 · In the original Transformer model, Decoder blocks have two attention mechanisms: the first is pure Multi Head Self-Attention, the second is Self-Attention with respect to Encoder's output. In GPT there is no Encoder, therefore I assume its blocks only have one attention mechanism. That's the main difference I found.
Feb 3, 2023 · ChatGPT is a type of language model that uses a transformer architecture, which includes both an encoder and a decoder. Specifically, GPT-3, the model on which ChatGPT is based, uses a transformer decoder architecture without an explicit encoder component. However, the transformer decoder can be thought of as both an encoder and a decoder ...
Jan 7, 2021 · BERT is a Transformer encoder, while GPT is a Transformer decoder: You are right in that, given that GPT is decoder-only, there are no encoder attention blocks, so the decoder is equivalent to the encoder, except for the masking in the multi-head attention block. There is, however, an extra difference in how BERT and GPT are trained:
Jul 4, 2019 · The network learns this encoding/decoding because the loss metric increases with the difference between the input and output image - every iteration, the encoder gets a little bit better at finding an efficient compressed form of the input information, and the decoder gets a little bit better at reconstructing the input from the encoded form.
AT is often applied to transfer information from encoder to decoder. I.e. decoder neurons receive addition input (via AT) from the encoder states/activations. So in this case AT connects 2 different components - encoder and decoder. If SA is applied - it doesn't connect 2 different components, it's applied within one component.
Encoder and decoder are highly overloaded terms. As a generic definition, an encoder-decoder neural architecture has a part of the network called "encoder" that receives an input and generates a code (i.e. expresses the input in a different representation space) and another part called "decoder" that takes a given code and converts it to the output representation space.
Jun 21, 2019 · I'm reading about Embedding layers, especially applied to NLP and word2vec, and they seem nothing more than an application of Autoencoders for dimensionality reduction.
Nov 26, 2021 · I was guessing whats the difference. I know following difference between encoder and decoder blocks: GPT Decoder looks only at previously generated tokens and learns from them and not in right side tokens. BERT Encoder gives attention to tokens on both sides. But I have following doubts: Q1. GPT2,3 focuses on new/one/zero short learning.
Feb 27, 2023 · The encoder is trying to do a similar thing - figure out what the important bits of context are to pass that on to the decoder. Essentially, we are trying to get the encoder to learn a way to represent everything relevant that has happened in the input sequence in this singular context vector of size [hidden_dims] .
