Transformer Structure for Processing Sentences All at Once
RNNs and LSTMs process words one at a time in sequence, which can slow down the process as sentences get longer and can cause earlier information to be poorly transmitted to later parts.
The Transformer model was developed to address these issues by processing all words in a sentence simultaneously, leading to faster training speeds.
It uses an Attention mechanism to grasp the context of the entire sentence and focus on the most important information.
The Transformer is the foundational structure for the GPT models that have sparked the generative AI boom, with the T in GPT standing for Transformer.
Thanks to these advantages, the Transformer model has demonstrated superior performance in various natural language processing (NLP) tasks such as translation, text summarization, question answering, and writing.
Attention Mechanism for Focusing on Important Information
The Attention mechanism is a technique that helps determine which words in a sentence are more important and allows the model to focus more on those words.
For example, in the sentence "I got a stomachache from eating ramen yesterday," to understand the cause of "stomachache," one should focus on the word "ramen."
Attention examines the entire sentence, identifies information relevant to the current word, and uses it to amplify its impact during learning.
Through this process, the Transformer can understand the full context of sentences, focus on important information, and make more accurate predictions.
First introduced in Google's 2017 paper Attention is All You Need, the Transformer model has shown outstanding performance in various NLP tasks, leading the field of generative AI.
In the next lesson, we'll engage in hands-on practice by building a simple RNN model.
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