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The "Attention is All You Need" paper introduced a new way for AI to read and understand language, much like how we might read a comic book. As you read each p

The "Attention is All You Need" paper introduced a new way for AI to read and understand language, much like how we might read a comic book. As you read each panel of a comic book, you don't just look at the words in the speech bubbles, but you also pay attention to who's talking, what they're doing, and what happened in the previous panels. You might pay more attention to some parts than others. This is sort of like what the Transformer model does with text. When the Transformer reads a sentence, it doesn't just look at one word at a time. It looks at all the words at once, and figures out which ones are most important to understand each other. This is called "attention." For example, in the sentence "The cat, which is black, sat on the mat," the Transformer model would understand that "cat" is connected to "black" and "sat on the mat." The "attention" part is very helpful because, like in a comic book, understanding one part of a sentence often depends on understanding other parts. This makes the Transformer model really good at understanding and generating language. Also, because the Transformer pays attention to all parts of the sentence at the same time, it can be faster than other models that read one word at a time. This is like being able to read a whole page of your comic book at once, instead of having to read each panel one by one.

QuantumGood 3y ago
Explaining it for a slightly older audience, a transformer is a type of artificial neural network designed for processing sequences, like sentences in a text. It's especially known for its use in natural language processing (NLP), which is the field of AI that deals with understanding and generating human language. The Transformer is unique because it uses a mechanism called "attention" to understand the relationships between words in a sentence, which works like this: (1) Encoding: First, the Transformer turns each word in a sentence into a list of numbers, called a vector. These vectors capture information about the word's meaning. (2) Self-Attention: Next, for each word, the Transformer calculates a score for every other word in the sentence. These scores determine how much each word should contribute to the understanding of the current word. This is the "attention" part. For example, in the sentence "The cat, which is black, sat on the mat," the words "cat" and "black" would get high scores when trying to understand the word "black" because they are closely related. (3) Aggregation: The Transformer then combines the vectors of all the words, weighted by their attention scores, to create a new vector for each word. This new vector captures both the meaning of the word itself and the context provided by the other words in the sentence. (4) Decoding: Finally, in a task like translation, the Transformer uses the vectors from the encoding phase to generate a sentence in the target language. It again uses attention to decide which words in the original sentence are most relevant for each word it's trying to generate in the new sentence. One key advantage of the Transformer is that it can calculate the attention scores for all pairs of words at the same time, rather than one at a time like previous models. This allows it to process sentences more quickly, which is important for large tasks like translating a whole book.
- rosebay 3y ago
  Great explanation
  - mahathu 3y ago
    It's chat-gpt generated. It's even leaked part of the prompt in the intro. I especially disagree with: > natural language processing (NLP), which is the field of AI that deals with understanding and generating human language.
    - uhfcjgx 3y ago
      Do you disagree with anything else? That sounds like a simplification and not too bad given the target audience.
    - armchairhacker 3y ago
      If GPT4 wrote this it did a great job, and highlights how incredible and useful it can be. Although I'm sure "ELI5 what is a transformer" was one of the RHLF prompts which got handcrafted responses from OpenAI engineers whose bread and butter is transformers, so...still a great response.
- QuantumGood 3y ago
  The importance of the "Attention is All You Need" paper by Vaswani et al., in 2017 is that it introduced the Transformer type of model architecture. The model is so named because it "transforms" one sequence into another. For example, in a machine translation task, it can transform a sentence in one language into a sentence in another language. The key innovation of the Transformer model is the use of self-attention mechanisms. This means that instead of processing the input sequence word by word, the model considers all the words in the sequence at the same time and learns to pay "attention" to the most important ones for the given task. In essence, the Transformer model is a design for building network architectures that can process data in parallel and focus on different parts of the data depending on the task at hand. The Transformer model has proven to be highly effective and flexible, and has been adopted in many variants and applications, including BERT, GPT, T5, and many others.
yawnxyz 3y ago
how is "attention" different from using tokens > vector database > cosine similarity?
- nycdatasci 3y ago
  If you're specifically focused on semantic similarity, I would say that attention adds to the dimensionality of the vector space. Distances between tokens can vary depending on context.
- kevinwang 3y ago
  It's orthogonal, right? How do you go from tokens to vector? Fully connected NN? lstm? Or transformer?
- QuantumGood 3y ago
  In the context of natural language processing, the attention mechanism used in Transformer models and the process of converting tokens to vectors and calculating cosine similarity have similarities but serve different purposes. When you convert words (tokens) into vectors and calculate cosine similarity, you're typically doing what's called "word embedding". This process captures the semantic meaning of words in a high-dimensional space. Words that have similar meanings have vectors that are close to each other in this space. Cosine similarity is a measure of how similar two vectors are, which in this context equates to how similar the meanings of two words are. On the other hand, the attention mechanism in Transformer models is a way to understand the relationships between words within a specific context. It determines how much each word in a sentence contributes to the understanding of every other word in the sentence. It's not just about the semantic similarity of words, but also about their grammatical and contextual relationships in the given sentence. Here's an analogy: imagine you're trying to understand a conversation between a group of friends. Just knowing the meaning of their words (like word embeddings do) can help you understand some of what they're saying. But to fully understand the conversation, you also need to know who's speaking to whom, who's agreeing or disagreeing with whom, who's changing the topic, and so on. This is similar to what the attention mechanism does: it tells the model who's "talking" to whom within a sentence. So while word embeddings and cosine similarity capture static word meanings, the attention mechanism captures dynamic word relationships within a specific context. Both are important for understanding and generating human language.
noobcoder 3y ago
I had a blog on it on overview of it with codes and explanation (more like eli15): https://medium.com/analytics-vidhya/googles-t5-transformer-theory-ffd0acc738d2 https://medium.com/analytics-vidhya/googles-t5-transformer-t...
dang 3y ago
Please don't post generated comments into HN threads. HN threads are for human discussion and we ban accounts that violate this.
- QuantumGood 3y ago
  Apologies. Seems like a good policy.

QuantumGood 3y ago

Explaining it for a slightly older audience, a transformer is a type of artificial neural network designed for processing sequences, like sentences in a text. It's especially known for its use in natural language processing (NLP), which is the field of AI that deals with understanding and generating human language. The Transformer is unique because it uses a mechanism called "attention" to understand the relationships between words in a sentence, which works like this: (1) Encoding: First, the Transformer turns each word in a sentence into a list of numbers, called a vector. These vectors capture information about the word's meaning. (2) Self-Attention: Next, for each word, the Transformer calculates a score for every other word in the sentence. These scores determine how much each word should contribute to the understanding of the current word. This is the "attention" part. For example, in the sentence "The cat, which is black, sat on the mat," the words "cat" and "black" would get high scores when trying to understand the word "black" because they are closely related. (3) Aggregation: The Transformer then combines the vectors of all the words, weighted by their attention scores, to create a new vector for each word. This new vector captures both the meaning of the word itself and the context provided by the other words in the sentence. (4) Decoding: Finally, in a task like translation, the Transformer uses the vectors from the encoding phase to generate a sentence in the target language. It again uses attention to decide which words in the original sentence are most relevant for each word it's trying to generate in the new sentence. One key advantage of the Transformer is that it can calculate the attention scores for all pairs of words at the same time, rather than one at a time like previous models. This allows it to process sentences more quickly, which is important for large tasks like translating a whole book.

rosebay 3y ago
Great explanation
- mahathu 3y ago
  It's chat-gpt generated. It's even leaked part of the prompt in the intro. I especially disagree with: > natural language processing (NLP), which is the field of AI that deals with understanding and generating human language.
  - uhfcjgx 3y ago
    Do you disagree with anything else? That sounds like a simplification and not too bad given the target audience.
  - armchairhacker 3y ago
    If GPT4 wrote this it did a great job, and highlights how incredible and useful it can be. Although I'm sure "ELI5 what is a transformer" was one of the RHLF prompts which got handcrafted responses from OpenAI engineers whose bread and butter is transformers, so...still a great response.
QuantumGood 3y ago
The importance of the "Attention is All You Need" paper by Vaswani et al., in 2017 is that it introduced the Transformer type of model architecture. The model is so named because it "transforms" one sequence into another. For example, in a machine translation task, it can transform a sentence in one language into a sentence in another language. The key innovation of the Transformer model is the use of self-attention mechanisms. This means that instead of processing the input sequence word by word, the model considers all the words in the sequence at the same time and learns to pay "attention" to the most important ones for the given task. In essence, the Transformer model is a design for building network architectures that can process data in parallel and focus on different parts of the data depending on the task at hand. The Transformer model has proven to be highly effective and flexible, and has been adopted in many variants and applications, including BERT, GPT, T5, and many others.

yawnxyz 3y ago

how is "attention" different from using tokens > vector database > cosine similarity?

nycdatasci 3y ago
If you're specifically focused on semantic similarity, I would say that attention adds to the dimensionality of the vector space. Distances between tokens can vary depending on context.
kevinwang 3y ago
It's orthogonal, right? How do you go from tokens to vector? Fully connected NN? lstm? Or transformer?
QuantumGood 3y ago
In the context of natural language processing, the attention mechanism used in Transformer models and the process of converting tokens to vectors and calculating cosine similarity have similarities but serve different purposes. When you convert words (tokens) into vectors and calculate cosine similarity, you're typically doing what's called "word embedding". This process captures the semantic meaning of words in a high-dimensional space. Words that have similar meanings have vectors that are close to each other in this space. Cosine similarity is a measure of how similar two vectors are, which in this context equates to how similar the meanings of two words are. On the other hand, the attention mechanism in Transformer models is a way to understand the relationships between words within a specific context. It determines how much each word in a sentence contributes to the understanding of every other word in the sentence. It's not just about the semantic similarity of words, but also about their grammatical and contextual relationships in the given sentence. Here's an analogy: imagine you're trying to understand a conversation between a group of friends. Just knowing the meaning of their words (like word embeddings do) can help you understand some of what they're saying. But to fully understand the conversation, you also need to know who's speaking to whom, who's agreeing or disagreeing with whom, who's changing the topic, and so on. This is similar to what the attention mechanism does: it tells the model who's "talking" to whom within a sentence. So while word embeddings and cosine similarity capture static word meanings, the attention mechanism captures dynamic word relationships within a specific context. Both are important for understanding and generating human language.

noobcoder 3y ago

I had a blog on it on overview of it with codes and explanation (more like eli15): https://medium.com/analytics-vidhya/googles-t5-transformer-theory-ffd0acc738d2 https://medium.com/analytics-vidhya/googles-t5-transformer-t...

dang 3y ago

Please don't post generated comments into HN threads. HN threads are for human discussion and we ban accounts that violate this.

QuantumGood 3y ago
Apologies. Seems like a good policy.