BERT, GPT & Language Models

The Language Model Revolution

BERT and GPT represent two paradigms in language modeling that transformed NLP. While both use transformers, their training objectives and applications differ fundamentally, leading to complementary strengths in understanding and generating language.

GPT: Generative Pre-trained Transformer

GPT models are autoregressive language models that predict the next token given previous tokens. This simple objective, when scaled up, leads to remarkable capabilities in text generation, few-shot learning, and reasoning.

Key Features:

  • Unidirectional: Only attends to previous tokens (causal masking)
  • Autoregressive: Generates text one token at a time
  • Objective: Predict next token P(x_t | x_1, ..., x_t-1)
  • Strengths: Generation, completion, few-shot learning

GPT Autoregressive Generation

Watch how GPT generates text token by token, using causal masking to ensure each position only sees previous tokens.

Generated Sequence:

The cat

BERT: Bidirectional Encoder Representations

BERT introduced bidirectional pre-training, allowing the model to see context from both directions. This makes it excellent for understanding tasks but not suitable for generation.

Key Features:

  • Bidirectional: Attends to all positions simultaneously
  • Masked LM: Predicts randomly masked tokens
  • Next Sentence: Predicts if sentences are consecutive
  • Strengths: Classification, NER, question answering

BERT Masked Language Modeling

Click any token to mask it and see BERT's predictions using bidirectional context. Toggle attention visualization to see how all tokens contribute.

Key Difference from GPT:

BERT can see tokens on both sides of [MASK], while GPT only sees previous tokens. This makes BERT better for understanding but unable to generate text naturally.

Architecture Comparison

GPT Architecture

  • • Decoder-only transformer
  • • Causal self-attention
  • • Left-to-right processing
  • • No encoder-decoder attention

BERT Architecture

  • • Encoder-only transformer
  • • Bidirectional self-attention
  • • Sees full context
  • • Cannot generate text naturally

Pre-training Objectives Comparison

Hover over each model to highlight their different pre-training approaches.

GPT Advantages

  • • Natural text generation
  • • Few-shot learning ability
  • • Can be prompted creatively

BERT Advantages

  • • Better understanding of context
  • • Superior for classification
  • • Bidirectional information flow

Training Objectives

GPT: Next Token Prediction

L_GPT = -Σ log P(x_i | x_1, ..., x_i-1)

Learns to predict each token given all previous tokens.

BERT: Masked Language Modeling

L_MLM = -Σ log P(x_masked | x_context)

Learns to predict masked tokens using bidirectional context.

Fine-tuning Strategies

GPT Fine-tuning

  • Add task-specific tokens/prompts
  • Continue autoregressive training
  • Few-shot learning via prompting

BERT Fine-tuning

  • Add task-specific classification head
  • Fine-tune entire model on labeled data
  • Different heads for different tasks

Evolution & Impact

GPT Family

  • GPT-2: Demonstrated zero-shot task performance
  • GPT-3: 175B parameters, few-shot learning
  • GPT-4: Multimodal, improved reasoning

BERT Family

  • RoBERTa: Optimized training approach
  • ALBERT: Parameter sharing for efficiency
  • DeBERTa: Disentangled attention mechanism

Why They Matter

  • Transfer Learning: Pre-train once, fine-tune for many tasks
  • Contextual Understanding: Words have different meanings in context
  • Scale Benefits: Larger models show emergent capabilities
  • Foundation Models: Base for countless applications
  • Democratization: Pre-trained models accessible to all