2.2 Attention Mechanism

The Attention Mechanism is a core technology of the transformer model, playing a vital role in enabling Large Language Models (LLMs) to deeply understand context. Unlike the sequential processing of traditional RNNs or LSTMs, the attention mechanism evaluates how each word in a sentence relates to all other words, capturing the overall context. This section focuses on the Self-Attention Mechanism.

In the previous section, "Explanation of the Transformer Model", we covered the structure and features of the transformer model. Here, we focus on the attention mechanism, the core technology for understanding context in LLMs.

What is the Self-Attention Mechanism?

The self-attention mechanism calculates the degree of dependency between each word and all other words in the input text. For example, if a word is strongly related to another word in the sentence, the "attention" paid to that word increases. This allows LLMs to capture relationships between words, even in long or complex contexts, enabling natural text generation and context understanding.

Concepts of Query, Key, and Value

The attention mechanism is based on three core concepts: Query, Key, and Value. Each word is transformed into these components and processed as follows:

• Query: The word to focus on
• Key: Information from other words in the sentence
• Value: The meaning derived from related words

The dot product of the query and key is computed, and based on the results, attention is assigned to the value. This calculation is performed for all words in the sentence, resulting in weights that indicate how much each word influences the overall context. This process enables the generation of coherent and contextually appropriate text.

Scaled Dot-Product Attention

The Scaled Dot-Product Attention is a key computation method in the attention mechanism. It involves calculating the dot product of the query and key to quantify the relationships between words. The resulting value is scaled (adjusted to an appropriate range) before applying the softmax function, which assigns attention scores to each word. This process ensures stability and efficiency even with long sequences and complex contexts.

Multi-Head Attention

The transformer model employs Multi-Head Attention, which processes the attention mechanism from multiple perspectives. This technique allows the model to capture diverse relationships and contexts that might be missed by a single attention mechanism. Each "head" independently calculates attention from a different viewpoint, and combining these results enables a richer understanding of the context.

The attention mechanism forms the foundation of LLMs' ability to understand and generate context with high precision. By combining self-attention and multi-head attention, LLMs can capture complex dependencies within long sequences, making them scalable and adaptable for various NLP tasks.

In the next section, "Overview of BERT, GPT, and T5 Models", we will introduce key models used in building LLMs. We’ll explain how these models utilize the attention mechanism and explore their unique features in NLP tasks.