4. Working Principle of Attention Models in Encoder-Decoder Architectures
By introducing the attention mechanism to the traditional encoder-decoder architecture, the neural network gains the ability to focus on specific parts of the input sequence, enabling enhanced understanding and improved translation accuracy. In this article, we delve into the working principles of attention models and highlight their significant advantages over traditional models without going into mathematical rigor.
1. INTRODUCTION
Attention mechanism is the core component of transformer models and plays a crucial role in LLM models.
Let’s consider the task of translating the sentence “the cat ate the mouse” from English to French. One approach is to use an encoder-decoder model, a popular choice for sentence translation.
Read the following for a revision of Encoder-Decoder Architecture:
The encoder-decoder model translates one word at a time, processing each word sequentially. However, a challenge arises when the words in the source language don’t align perfectly with the words in the target language.
For example, let’s take the sentence “Black cat ate the mouse.” In this case, the first English word is “black,” but in the translation, the first French word is “chat,” which means “cat” in English.
How can we train a model to focus more on the…
