Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!

StatQuest with Josh Starmer

Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!! - YouTube

Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!! StatQuest with Josh Starmer

Historical Context and Transformer Evolution

  • 2017: First Transformer had encoder + decoder parts connected for seq2seq translation
    • Example: "pizza is great" → "La pizza es magnifico"
    • Encoder crunches numbers to encode input, decoder decodes to translation
  • Post-2017 discovery: Both parts work independently
    • Decoder-only → basis for ChatGPT (text generation)
    • Encoder-only → basis for BERT and many other models
  • Decoder-only models gained popularity (ChatGPT, Gemini), overshadowing encoder-only
  • Question: Do people still use encoder-only Transformers? Answer: Yes, significant understated power

Word Embeddings Fundamentals

  • Purpose: Convert tokens (words, word bits, symbols) into numbers for neural networks
  • Simple approach: Assign random numbers to each word
    • Problem: Similar words (great/awesome) get different numbers
    • Neural network needs more complexity, can't transfer learning between similar words
  • Better approach: Similar words should get similar numbers
    • Same word in different contexts needs multiple numbers
    • Example: "great" positive vs sarcastic negative usage

Neural Network Word Embedding Training

  • Simple neural network setup:
    • Input for each unique word
    • Two activation functions → two word embeddings per word
    • Weights initialized with random numbers
    • Training data: "Pizza is great" and "pizza is awesome"
  • Training objective: Each word predicts next word
    • Pizza → is
    • is → great/awesome
  • Result after training: great and awesome cluster together (similar embeddings)
  • More context improves embeddings: Use multiple preceding words instead of just one
    • Example: "the pizza came out" → "of"

Positional Encoding

  • Problem: Current approach ignores word order
    • "the pizza came out of" = "pizza out came the of" (same inputs/outputs)
  • Solution: Positional encoding tracks word order
  • Critical importance: "Squatch eats pizza" vs "pizza eats Squatch"
    • Same words, completely different meanings
  • Multiple implementation methods (details out of scope)

Self-Attention Mechanism

  • Purpose: Track relationships among words
  • Example: "the pizza came out of the oven and it tasted good"
    • Word "it" could refer to pizza or oven
    • Attention correctly associates "it" with "pizza"
  • How it works:
    • Calculates similarity between each word and all other words (including itself)
    • Uses similarities to determine encoding impact
    • Words with higher similarity scores have larger impact on encoding

Context-Aware Embeddings

  • Combination of three layers:
    • Word embedding (converts to numbers)
    • Positional encoding (tracks word order)
    • Self-attention (establishes word relationships)
  • Result: Context-aware/contextualized embeddings
  • Capability: Cluster similar sentences or documents (not just words)

Applications

Retrieval Augmented Generation (RAG)

  • Break documents into text blocks
  • Create context-aware embeddings for each block
  • When given prompt "what is pizza", find most similar text chunks
  • Foundation for document search and retrieval

Classification Tasks

  • Use context-aware embeddings as inputs to neural networks
  • Example: Sentiment analysis (positive/negative sentiments about pizza on LinkedIn)
  • Alternative: Use embeddings in logistic regression for classification

Key Advantages

  • Encoder-only Transformers create versatile context-aware embeddings
  • Wide variety of applications beyond text generation
  • Significant but understated power compared to decoder-only models