Transformers: How Self-Attention & Positional Encoding Unlock AI Understanding

I. The Core Function of Transformers: Understanding Context

• Core Task: Enable AI to genuinely understand the contextual relationships within language.
  • Example: Recognizing what "it" refers to or what a "but" is contrasting.
• Traditional Model Limitations: Acted like old-fashioned tape recorders, forgetting earlier parts of a sentence, especially in long texts.

II. Two Breakthrough Designs

1. Self-Attention Mechanism: Resolving Word Relationships

   • Function: Automatically associates each word with all important words in the sentence.
     • Example: In the sentence "The cat sat on the mat because it was soft," the brain instantly connects "it" to "mat."
   • Working Principle: Calculates an "association score" with every word in the sentence, then integrates information from the highest-scoring words.

2. Positional Encoding: Addressing Word Order

   • Function: Assigns each word a "positional ID" (1st, 2nd, etc.).
     • Analogy: Like assigning number labels to packages in a warehouse to ensure "Beijing → Shanghai" is distinct from "Shanghai → Beijing."
   • Importance: Without it, "dog bites man" and "man bites dog" would be the same to AI.

III. Overall Architecture: A Factory Assembly Line

• Key Features:
  • Each layer of the assembly line has the same structure and can be stacked multiple times.
  • All words are processed in parallel, significantly improving efficiency compared to traditional models.
• Process: Input sentence undergoes word embedding and positional encoding, then passes through multiple layers of encoders (self-attention and feed-forward networks) to produce a condensed semantic representation. This is then fed to a decoder to generate the output word by word.

IV. Why Transformers are Revolutionary

• Comparison:
  • Traditional Models (RNN): Like flipping through a book, forgetting previous pages.
  • Transformers: Like laying out the entire book, allowing for constant reference to context.
• Speed: Traditional models calculate sequentially, while transformers calculate all words simultaneously, resulting in 10x+ speed improvements.
• Text Length: Traditional models struggle with long texts, while transformers easily handle texts of 5000+ words.

V. One-Sentence Summary

• Transformer = Self-Attention (understanding the network of word relationships) + Positional Encoding (remembering the "seat number" of each word). This enables AI to truly understand context.