The transformer model is like a superhero that saves the day by understanding languages and provides outputs based on our inputs. It does all this without the power of recurrent neural networks or convoluted neural networks, which are like its older superhero siblings.
Here’s how the transformer does its magic:
It takes a sentence as input and breaks it down into words (or pieces of words).
Then, it turns all those words into numbers (or ’embeddings’) in a really big game of ‘connect the dots’. A word is associated with a point in space in this game, and similar words stay close together while different words stay far apart.
The hero then plays a game of attention, where it pays extra attention to some words while ignoring others. This is done in multiple rounds (or ‘heads’). This helps it understand which words in a sentence are more important or related to each other.
It then puts these details through its intelligence filters (called ‘feed forward networks’) to gather valuable knowledge.
While all this is happening, it keeps track of the order of the words in the sentence, using mathematical superheroes (sine and cosine functions).
After all this, it uses its magic to predict the next word (softmax function), and this is how it can provide an output
Remember, its older superhero siblings, recurrent or convoluted neural networks, used to take a lot of time and energy to do this. But, our transformer hero is so awesome that it not only does this faster but also in a smarter and easier way! Even when it comes to long and complicated sentences, it has a strategic approach and does not get confused.
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I like your explanation! Here is how ChatGPT 3.5 explained it to me:
GPT stands for “Generative Pre-trained Transformer.” It refers to a type of artificial intelligence language model developed by OpenAI. An AI language model is like a “smart” computer program that can understand and generate coherent and clever human-like text. It is trained on vast amounts of written text from the internet and other sources, which helps it learn patterns, grammar, and context in language.
The “Generative” part means that the model can generate its own text based on a given prompt. It can complete sentences, answer questions, or even create original content.
The “Pre-trained” aspect means that the model has already been taught a lot about language by seeing and analyzing massive amounts of text from various sources, such as books, articles, and websites. This pre-training phase helps it gain knowledge and understanding about how words and sentences fit together. The more text the model is exposed to, the more it learns about grammar, semantics, and the context in which words are used.
Finally, “Transformer” refers to the specific architecture used in the model, which allows it to process and understand the context of text in a sophisticated way.
The Transformer architecture uses a technique called “self-attention” to give importance to different words or parts of the text based on their relevance. It pays attention to the words that are crucial for understanding the context and meaning of a sentence, while ignoring or giving less importance to irrelevant words. By using this approach, the Transformer helps the GPT model capture the relationships between words, understand sentence structures, and grasp the overall meaning of a piece of text.