57
votes
Accepted
Why does the transformer do better than RNN and LSTM in long-range context dependencies?
I'll list some bullet points of the main innovations introduced by transformers , followed by bullet points of the main characteristics of the other architectures you mentioned, so we can then ...
- 5,183
29
votes
Accepted
How can Transformers handle arbitrary length input?
Actually, there is usually an upper bound for inputs of transformers, due to the inability of handling long-sequence. Usually, the value is set as 512 or 1024 at current stage.
However, if you are ...
- 451
28
votes
Accepted
How is BERT different from the original transformer architecture?
What is a transformer?
The original transformer, proposed in the paper Attention is all you need (2017), is an encoder-decoder-based neural network that is mainly characterized by the use of the so-...
- 39.1k
27
votes
What is the intuition behind the dot product attention?
Let's start with a bit of notation and a couple of important clarifications.
$\mathbf{Q}$ refers to the query vectors matrix, $q_i$ being a single query vector associated with a single input word.
$\...
- 5,183
23
votes
Accepted
What exactly are the "parameters" in GPT-3's 175 billion parameters and how are they chosen/generated?
Parameters is a synonym for weights, which is the term most people use for a neural networks parameters (and indeed in my experience it is a term that machine learners will use in general whereas ...
- 4,600
17
votes
Accepted
Can the decoder in a transformer model be parallelized like the encoder?
Can the decoder in a transformer model be parallelized like the
encoder?
Generally NO:
Your understanding is completely right. In the decoder, the output of each step is fed to the bottom decoder in ...
- 549
16
votes
Accepted
How does the (decoder-only) transformer architecture work?
Introduction
Large-language models (LLMs) have gained tons of popularity lately with the releases of ChatGPT, GPT-4, Bard, and more. All these LLMs are based on the transformer neural network ...
- 1,898
14
votes
Accepted
What kind of word embedding is used in the original transformer?
I have found a good answer in this blog post The Transformer: Attention Is All You Need:
we learn a “word embedding” which is a smaller real-valued vector representation of the word that carries some ...
- 555
14
votes
Why does GPT-2 Exclude the Transformer Encoder?
GPT-2 is a close copy of the basic transformer architecture.
GPT-2 does not require the encoder part of the original transformer architecture as it is decoder-only, and there are no encoder attention ...
- 1,084
10
votes
Accepted
Why does this multiplication of $Q$ and $K$ have a variance of $d_k$, in scaled dot product attention?
In statistics, if $X$ and $Y$ are independent and randomly distributed variables:
$\mathbb{E}[X + Y] = \mathbb{E}[X] + \mathbb{E}[Y] \\
Var(X + Y) = Var(X) + Var(Y) \\
\mathbb{E}[XY] = \mathbb{E}[X]\...
- 1,510
9
votes
Why does the transformer do better than RNN and LSTM in long-range context dependencies?
Let's start with RNN. A well known problem is vanishin/exploding gradients, which means that the model is biased by most recent inputs in the sequence, or in other words, older inputs have practically ...
- 276
9
votes
Can the decoder in a transformer model be parallelized like the encoder?
Can the decoder in a transformer model be parallelized like the encoder?
The correct answer is: computation in a Transformer decoder can be parallelized during training, but not during actual ...
- 351
9
votes
Accepted
In Computer Vision, what is the difference between a transformer and attention?
The original transformer is a feedforward neural network (FFNN)-based architecture that makes use of an attention mechanism. So, this is the difference: an attention mechanism (in particular, a self-...
- 39.1k
8
votes
Accepted
What is the purpose of Decoder mask (triangular mask) in Transformer?
The Transformer model presented in this tutorial is an auto-regressive Transformer. Which means that prediction of next token only depends on it's previous tokens.
So in order to predict next token, ...
- 260
8
votes
Accepted
What is different in each head of a multi-head attention mechanism?
The reason each head is different is because they each learn a different set of weight matrices $\{ W_i^Q, W_i^K, W_i^V \}$ where $i$ is the index of the head. To clarify, the input to each attention ...
- 1,510
8
votes
Accepted
Why does a transformer not use an activation function following the multi-head attention layer?
This goes back to the purpose of self-attention.
Measure between word-vectors is generally computed through cosine-similarity because in the dimensions word tokens exist, it's highly unlikely for two ...
- 476
8
votes
How to assess if OpenAI's ChatGPT chatbot has a human in the loop?
Until you can prove that OpenAI has HITL in ChatGPT, it is just an idea with no basis. It's not up to us to disprove it, it's up to you to prove it.
Let me address your points:
You seem to be basing ...
- 189
7
votes
Accepted
What is the cost function of a transformer?
I took a look at the Tensor2Tensor's source code implementation, and it seems like the loss function is the cross-entropy between the predicted probability matrix $\|\text{sentence length}\| \times \|\...
- 1,510
7
votes
Accepted
Transformers: How to use the target mask properly?
The main issue during training is that you haven't right-shifted the input of the decoder, which is probably why you set the diagonals of mask to -inf (when it ...
- 1,510
7
votes
Accepted
Why are biases (typically) not used in attention mechanism?
For certain types of layers, such as transformers and convolutional layers, including a bias term is unnecessary and adds unnecessary overhead to the model.
The reason for this is that these layers ...
- 94
7
votes
Can you confirm that the transformer works strictly deterministically and there is no randomness inside or between the attention layers?
Can you confirm that the transformer works strictly deterministically and there is no randomness inside or between the attention layers?
Of course, there is no injected randomness in a regular ...
- 2,180
5
votes
What is the purpose of Decoder mask (triangular mask) in Transformer?
We give the target input into the transformer decoder while training the model. So it is easy for the model to "peek ahead" and learn what the next word would be. To ensure that this doesn't ...
- 151
5
votes
Accepted
What is the difference between the positional encoding techniques of the Transformer and GPT?
The purpose of introduction of positional encoding is to insert a notion of location of a given token in the sequence. Without it, due to the permutation equivariance (symmetry under the token ...
- 2,494
5
votes
What kind of word embedding is used in the original transformer?
No, neither Word2Vec nor GloVe is used as Transformers are a newer class of algorithms. Word2Vec and GloVe are based on static word embeddings while Transformers are based on dynamic word embeddings.
...
- 1,853
5
votes
Why does GPT-2 Exclude the Transformer Encoder?
The cases when we use encoder-decoder architectures are typically when we are mapping one type of sequence to another type of sequence, e.g. translating French to English or in the case of a chatbot ...
- 71
5
votes
What is the Intermediate (dense) layer in between attention-output and encoder-output dense layers within transformer block in PyTorch implementation?
Feedforward layer is an important part of the transformer architecture.
Transformer architecture, in addition to the self-attention layer, that aggregates ...
- 2,494
5
votes
Accepted
Do Vision Transformers handle arbitrary sequence lengths the same way as normal Transformers?
Yes, they can handle sequences with arbitrary length sequence, but with some remarks.
In the paper Training data-efficient image transformers & distillation through attention authors train models ...
- 2,494
5
votes
Does ChatGPT use different transformers for different downstream tasks?
It is just one huge model which performs autoregressive text generation.
The ability to perform a wide variety of task, defined at inference time is called in-context learning and was introduced in ...
- 242
5
votes
Can someone help me understand the intuition behind the query, key and value matrices in the transformer architecture?
A very very distant connection can be seen between the self-attention layer and the word2vec model. I think that this might be helpful to in order to gain more intution.
Starting from the word2vec ...
- 600
4
votes
Why don't people use nonlinear activation functions after projecting the query key value in attention?
It seems like doing this would lead to much-needed nonlinearity, otherwise, we're just doing linear transformations.
Attention is broadly defined as a following operation ($\text{softmax}$ is ...
- 2,416
Only top scored, non community-wiki answers of a minimum length are eligible