decoding

Transformers have revolutionized natural language processing with their use of self-attention mechanisms. In this post, we will study the key components of transformers to understand how they have become the basis of the state of the art in different tasks.  

Introduction: Attention is all you need 

The Transformer architecture was first introduced in the 2017 paper “Attention is All You Need” by researchers at Google. Unlike previous sequence models such as RNNs, Transformer relies entirely on self-attention to model dependencies in sequential data like text.   

Large language models knowledge test

Remarkably, this simple change led to major improvements in machine translation quality over existing methods. Since then, Transformers have been applied successfully to diverse NLP tasks like text generation, summarization, and question-answering. Their versatility has even led to applications in computer vision.  

But what exactly is self-attention and why is it so effective? Let’s explore this. 

The limitations of Recurrent Neural Networks – RNNs   

Recurrent neural networks (RNNs) used to be the dominant approach for modeling sequences. An RNN processes textual data incrementally, maintaining a “memory” of the previous context. For example, to predict the next word in a sentence, an RNN model would incorporate information about all the preceding words.  

However, RNNs have certain limitations. They process data sequentially, making parallelization difficult. More critically, they struggle to learn long-range dependencies because the information gets diluted over many steps of time. Attention mechanisms were proposed to mitigate this issue. 

Why use a transformer model?  

The transformer architecture has enabled the development of new models that can be trained on large datasets and significantly outperform recurrent neural networks like LSTMs. These new models are utilized for tasks like sequence classification, question answering, language modeling, named entity recognition, summarization, and translation.  

Let’s examine the key components of transformers to understand how they have become the foundation for state-of-the-art performance on different NLP tasks.  

Transformer design  

A transformer consists of an encoder and a decoder. The encoder’s role is to encode the inputs (i.e. sentences) into a state, often containing multiple tensors. This state is then passed to the decoder to generate the outputs.

In machine translation, the encoder converts a source sentence, e.g. “Hello world“, into a state, such as a vector, that captures its semantic meaning.

The decoder then utilizes this state to produce the translated target sentence, e.g. “Bonjour le monde.” Both the encoder and decoder primarily employ Multi-Head Attention and Feedforward Networks, which are the focus of this article.   

Key transformer components  

1. Input embedding  

Embedding aims to create a vector representation of words where words with similar meanings will be close in terms of Euclidean distance. For instance, the words “bathroom” and “shower” are related to the same concept, so their word vectors are close in Euclidean space as they convey similar meanings.  

For the encoder, the authors opted for an embedding size of 512 (i.e. each word is represented by a 512-dimensional vector).  

2. Positional encoding  

The position of a word plays a crucial role in understanding the sequence we want to model.  

Therefore, we add positional information about the word’s location in the sequence to its vector. The authors used the following sinusoidal. 

We will explain positional encoding in more detail with an example.  

We note the position of each word in the sequence.  

We define dmodel = 512, which represents the size of the embedding vector of each word (i.e. the vector dimension). We can now rewrite the two positional encoding equations as:  

We can see that the wavelength (i.e. frequency) λt decreases as the dimension increases, this forms a progression along the wave from 2pi to 10000.2pi.  

In this model, the absolute positional information of a word in a sequence is added directly to its initial vector. For this, the positional encoding must have the same size dmodel as the initial word vector.  

3. Attention mechanism  

• Scaled Dot-Product Attention  

Let’s explain the attention mechanism. The key goal of attention is to estimate the relative relevance of the keywords compared to the query word for the same entity. For this, the attention mechanism takes a query vector Q representing a word, the keys K comprising all other words in the sentence, and values V representing the word vectors.  

In our case, V = Q (for the two self-attention layers). In other words, the attention mechanism provides the significance of a word in a given sentence.  

When we compute the normalized dot product between the query and the keys, we get a tensor that represents the relative importance of each other word for the query. To go deeper into mathematics, we can try to understand why the authors used a dot product to calculate the relation between two words.  

A word is represented by a vector in Euclidian space, in this case, a vector of size 512.   

When computing the dot product between Q and KT, we calculate the product between Q’s orthogonal projection onto K. In other words, we estimate the alignment between the query and keyword vectors, returning a weight for each word in the sentence.  

We then normalize by dk to counteract large Q and K magnitudes which can push the softmax function into regions with tiny gradients. The softmax function regularizes the terms and rescales them between 0 and 1 (i.e., converts the dot product to a probability distribution), with the goal of normalizing all weights between 0 and 1.  

Finally, we multiply the weights (i.e., importance) by the values V to reduce irrelevant words and focus on the most significant words.  

• Multi-Head Attention  

The key idea is that attention is applied multiple times in parallel on different projections of the input queries, keys, and values. This allows the model to learn different types of dependencies between the input words.  

The input queries (Q), keys (K), and values (V) are each linearly projected h times into smaller subspaces. For example, h=8 times into 64-dimensional spaces.  

Attention is then applied in each of these h projected subspaces in parallel, yielding h different attention outputs.  

These h outputs are concatenated and linearly projected again to get the final values. The projections allow the model to focus on different positional and semantic relationships between words since each projected subspace captures different information.  

Doing this in parallel (multi-head) instead of sequentially improves efficiency.  

The projection matrices are learned during training to discover the most useful projections. So, in summary, multi-head attention applies the attention mechanism in multiple parallel subspaces to learn different types of dependencies between words in an efficient way.  

Let’s dive into the mechanics of encoder-decoder architecture.  

In this section, we’ll explain how the encoder and decoder work together to translate an English sentence into a French one, step by step.  

1. Encoder  

• Convert a sequence of tokens to a sequence of vectors by using embeddings.    

• Add position information in each word vector.  

The key advantage of recurrent neural networks is their knack for understanding relationships between sequences and remembering information. On the other hand, Transformers employ positional encoding to factor in where words are in a sequence.  

• Apply Multi-Head Attention  

• Use Feed Forward Network  

2. Decoder  

• Utilize embeddings to transform a French sentence into vectors.   

• Add positional details within each word vector.    

• Apply Multi-Head Attention  

• Apply Feed Forward Network  

• Apply Multi-Head Attention to the encoder output.  

We can observe that the Transformer combines the encoder’s output with the decoder’s input. This enables it to discern the relationship between the vectors that encode the English and French sentences.  

• Apply the Feed Forward Network again.  
• Compute the probability for the next word by using linear + SoftMax block. The decoder returns the highest probability as the next word at the output.  

In our case, the next word after “Je” is “suis”.  

Final thoughts 

The transformer model outperforms all the models on different benchmarks also there was no difference seen between the translation provided by the algorithm and by humans.   

Transformers are a major advance in NLP, they exceed RNN by having a lower training cost allowing to train models on larger corpora. Even today, transformers remain the basis of state-of-the-art models such as BERT, Roberta, XLNET, and GPT.  

References: 

https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf 

https://github.com/hkproj/transformer-from-scratch-notes 

http://jalammar.github.io/illustrated-transformer/ 

Transformer models are a type of deep learning model that is used for natural language processing (NLP) tasks. They can learn long-range dependencies between words in a sentence, which makes them very powerful for tasks such as machine translation, text summarization, and question answering.

Transformer models work by first encoding the input sentence into a sequence of vectors. This encoding is done using a self-attention mechanism, which allows the model to learn the relationships between the words in the sentence.

Once the input sentence has been encoded, the model decodes it into a sequence of output tokens. This decoding is also done using a self-attention mechanism.

The attention mechanism is what allows transformer models to learn long-range dependencies between words in a sentence. The attention mechanism works by focusing on the most relevant words in the input sentence when decoding the output tokens.

Learn in detail about transformer models here:

Transformer models are very powerful, but they can be computationally expensive to train. However, they are constantly being improved, and they are becoming more efficient and powerful all the time.

History

The history of transformers in neural networks can be traced back to the early 1990s when Jürgen Schmidhuber proposed the first transformer model. This model was called the “fast weight controller” and it used a self-attention mechanism to learn the relationships between words in a sentence. However, the fast-weight controller was not very efficient, and it was not widely used.

In 2017, Vaswani et al. published the paper “Attention is All You Need”, which introduced a new transformer model that was much more efficient than the fast-weight controller. This new model, which is now simply called the “transformer”, quickly became state-of-the-art for a wide range of natural language efficient (NLP) tasks, including machine translation, text summarization, and question answering.

Learn more about NLP in this blog —-> Applications of Natural Language Processing

The transformer has been so successful because it can learn long-range dependencies between words in a sentence. This is essential for many NLP tasks, as it allows the model to understand the context of a word in a sentence. The transformer does this using a self-attention mechanism, which allows the model to focus on the most relevant words in a sentence when decoding the output tokens.

The transformer has had a major impact on the field of NLP. It is now the go-to approach for many NLP tasks, and it is constantly being improved. In the future, transformers are likely to be used to solve a wider range of NLP tasks, and they will become even more efficient and powerful.

Here are some of the key events in the history of transformers in neural networks:

• 1990: Jürgen Schmidhuber proposes the first transformer model, the “fast weight controller”.
• 2017: Vaswani et al. publish the paper “Attention is All You Need”, which introduces the transformer model.
• 2018: Transformer models achieve state-of-the-art results on a wide range of NLP tasks, including machine translation, text summarization, and question answering.
• 2019: Transformers are used to create large language models (LLMs) such as BERT and GPT-2.
• 2020: LLMs are used to create even more powerful models such as GPT-3.

The history of transformers in neural networks is still being written. It is an exciting time to be in the field of NLP, as transformers are making it possible to solve previously intractable problems.

NLP transformer architecture

The transformer model is made up of two main components: an encoder and a decoder. The encoder takes the input sentence as input and produces a sequence of vectors. The decoder then takes these vectors as input and produces the output sentence.

The encoder consists of a stack of self-attention layers. Each self-attention layer takes a sequence of vectors as input and produces a new sequence of vectors. The self-attention layer works by first computing a score for each pair of words in the input sequence. The score for a pair of words is a measure of how related the two words are. The self-attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.

The decoder consists of a stack of self-attention layers and a recurrent neural network (RNN). The self-attention layers work the same way as in the encoder. The RNN takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

The attention mechanism is what allows the transformer model to learn long-range dependencies between words in a sentence. The attention mechanism works by focusing on the most relevant words in the input sentence when decoding the output tokens.

For example, let’s say we want to translate the sentence “I love you” from English to Spanish. The transformer model would first encode the sentence into a sequence of vectors. Then, the model would decode the vectors into a sequence of Spanish words. The attention mechanism would allow the model to focus on the words “I” and “you” in the English sentence when decoding the Spanish words “te amo”.

Transformer models are a powerful tool for NLP, and they are constantly being improved. They are now the go-to approach for many NLP tasks, and they are constantly being improved.

Encoding and Decoding

Encoding and decoding are two key concepts in natural language processing (NLP). Encoding is the process of converting a sequence of words into a sequence of vectors. Decoding is the process of converting a sequence of vectors back into a sequence of words.

Encoding

The encoder in a transformer model takes a sequence of words as input and produces a sequence of vectors. The encoder consists of a stack of self-attention layers. Each self-attention layer takes a sequence of vectors as input and produces a new sequence of vectors. The self-attention layer works by first computing a score for each pair of words in the input sequence. The score for a pair of words is a measure of how related the two words are. The self-attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.

For example, let’s say we have the sentence “I like you”. The encoder would first compute a score for each pair of words in the sentence. The score for the word “I” and the word “like” would be high, because these words are related. The score for the word “like” and the word “you” would also be high, for the same reason. The encoder would then use these scores to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence “I like you”.

Decoding

The decoder in a transformer model takes a sequence of vectors as input and produces a sequence of words. The decoder also consists of a stack of self-attention layers. The self-attention layers work the same way as in the encoder. The decoder also has an RNN, which takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

For example, let’s say we want to translate the sentence “I love you” from English to Spanish. The decoder would first take the vector that represents the meaning of the sentence “I love you” as input. Then, the decoder would use the self-attention layers to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence “I love you” in Spanish. The decoder would then use the RNN to produce a sequence of Spanish words. The output of the RNN would be the Spanish sentence “Te amo”

Encoder only models

Encoder-only models are a type of transformer model that only has an encoder. Encoder-only models are typically used for tasks like text classification, where the model only needs to understand the meaning of the input text.

For example, an encoder-only model could be used to classify a news article as either “positive” or “negative”. The encoder would first encode the article into a sequence of vectors. Then, the model would use a classifier to classify the article.

Encoder-only models are typically less powerful than full transformer models, but they are much faster and easier to train. This makes them a good choice for tasks where speed and efficiency are more important than accuracy.

Decoder only models

Decoder-only models are a type of transformer model that only has a decoder. Decoder-only models are typically used for tasks like machine translation, where the model needs to generate the output text.

For example, a decoder-only model could be used to translate a sentence from English to Spanish. The decoder would first take the English sentence as input. Then, the decoder would use the self-attention layers to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence in Spanish. The decoder would then use an RNN to produce a sequence of Spanish words. The output of the RNN would be the Spanish sentence.

Decoder-only models are typically less powerful than full transformer models, but they are much faster and easier to train. This makes them a good choice for tasks where speed and efficiency are more important than accuracy.

Here is a table that summarizes the differences between encoder-only models and decoder-only models:

What are transformer models built of

Transformer models are built of the following components:

• Embedding layer: The embedding layer converts the input text into a sequence of vectors. The vectors represent the meaning of the words in the text.
• Self-attention layers: The self-attention layers allow the model to learn long-range dependencies between words in a sentence. The self-attention layers work by computing a score for each pair of words in the sentence. The score for a pair of words is a measure of how related the two words are. The self-attention layers then use these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.
• Positional encoding: The positional encoding layer adds information about the position of each word in the sentence. This is important for learning long-range dependencies, as it allows the model to know which words are close to each other in the sentence.
• Decoder: The decoder takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

Transformer models are also typically trained with the following techniques:

• Masked language modeling: Masked language modeling is a technique used to train transformer models to predict the missing words in a sentence. This helps the model to learn to attend to the most relevant words in a sentence.
• Attention masking: Attention masking is a technique used to prevent the model from attending to future words in a sentence. This is important for preventing the model from learning circular dependencies.
• Gradient clipping: Gradient clipping is a technique used to prevent the gradients from becoming too large. This helps to stabilize the training process and prevent the model from overfitting.

Attention layers are a type of neural network layer that allows the model to learn long-range dependencies between words in a sentence. The attention layer works by computing a score for each pair of words in the sentence. The score for a pair of words is a measure of how related the two words are. The attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the attention layer.

The input to the attention layer is a sequence of vectors. The output of the attention layer is a weighted sum of the input vectors. The weights are computed using the scores for each pair of words in the sentence.

The attention layer can learn long-range dependencies because it allows the model to attend to any word in the sentence, regardless of its position. This is in contrast to recurrent neural networks (RNNs), which can only attend to words that are close to the current word.

Transformer architecture is a neural network architecture that is based on attention layers. Transformer models are typically made up of an encoder and a decoder. The encoder takes the input text as input and produces a sequence of vectors. The decoder takes the output of the encoder as input and produces a sequence of output tokens.

The encoder consists of a stack of self-attention layers. The decoder also consists of a stack of self-attention layers. The self-attention layers in the decoder can attend to both the input text and the output text. This allows the decoder to generate the output text in a way that is consistent with the input text.

Transformer models are typically trained with the masked language modeling technique. Masked language modeling is a technique used to train transformer models to predict the missing words in a sentence. This helps the model to learn to attend to the most relevant words in a sentence.