Jul 13, 2023 · The reason you'd use encoder-decoder models over decoder-only models is if you expect the inputs to differ in nature wrt the outputs somehow. What T5 had was a "task/query encoder", whereas the decoder was the actual generative model. T5 was a step in blurring the line between NLP tasks and pure language modeling.

Overall, the encoder-decoder architecture has the benefit that (in theoretical terms) the encoder can analyse the context much better than the decoder because of its bidirectional context. This is actually very sweet for tasks where there is a natural way of separating the sequences into two components (like e.g. translation).

Sep 20, 2023 · See there is definitely a difference in architecture of encoder decoder and decoder only models. In encoder decoder models. The encoder and decoder is 2 separate neural networks. I hope you understand what multi layer neural networks are. In decoder only models the decoder is a single deep neural network. In encoder decoder models the prompt ...

Feb 10, 2023 · For instance, you have encoder-decoder for translation (sequence-to-sequence) in the original Attention is All You Need paper. Basically useful if you need to generate the output in some auto-regressive fashion (since you don't know how long the output is ex-ante), and you you want it to "align" with the input.

May 15, 2024 · Encoder-decoder vs decoder only Transformer Not sure if it has been already asked. It has just recently occured to me, whilst trying to explore finetuning a model for classification, NER etc, that I know little about the difference between these 2 architectures, and what are the differences in use case for these 2 architectures.

Oct 27, 2020 · The decoder takes in a multi-bit input and sets high a bit in position that corresponds to the value you sent in. For a 2-4 decoder that means sending in the value 2’b00 would set the output value as 4’b0001, 2’b01 would set output value to 4’b0010, etc. A multiplexer takes in a number of inputs.

May 21, 2020 · The general idea of a decoder is to parse the output sequence B together with the contextual information from the encoder and effectively find the relationship between the enoded input and the required output. This explains the difference between an arbitrary NN with a hidden layer and a true encoder-decoder architecture.

Jan 3, 2024 · The mouse wheel encoder and mouse wheel button micro switch are shown. the mouse wheel must be positioned between them. There are two types of scroll-wheels that are used: Optical and Mechanical. The optical scroll-wheel uses an optical encoder.

Jan 20, 2021 · Nice answer. Just expanding a bit: BERT is an encoder while GPT is a decoder but if you look closely they are basically the same architecture: GPT is a decoder where the cross (encoder-decoder) attention layer has been dropped (because there is no encoder ofc), so BERT and GPT are almost the same.

Feb 24, 2023 · Decoder-only models are easier to pretrain in an unsupervised manner based on crawled corpus? However, encoder-based models such as BERT can also be pre-trained based on crawled corpus using the random MASK strategy( feed on both sizes ).