BERT vs GPT: Interactive Transformer Architecture & Attention Lab

Sentence:

Disambiguation & Flow Presets:

Attention Head:

BERT (Encoder-Only)

Bidirectional Representations from Transformers (Devlin et al., 2018)

BIDIRECTIONAL

Input Sequence + Special Tokens (Click to inspect focus) 8 tokens

Full Unmasked Attention Matrix (NxN)

Hover cell: token_i → token_j

Visibility: 100% (Past, Current & Future)

0.0

1.0

Objective: Masked LM (MLM) + NSP

BERT masks 15% of tokens with [MASK] and reconstructs them using context from both the left and right simultaneously.

Attention Mask

Unmasked (All 1s)

Target Word Meaning

Riverbank (Geog)

GPT (Decoder-Only)

Generative Pre-trained Transformer (Radford et al., 2018 / OpenAI)

CAUSAL / AUTOREGRESSIVE

Causal Sequence (Click to view causal horizon) 7 tokens

Lower-Triangular Causal Mask Matrix

Hover cell: token_i → token_j

Upper Triangle: Masked (-∞ / 0.0)

0.0

1.0

Objective: Causal Language Modeling (CLM)

GPT optimizes $P(w_t \mid w_1, \dots, w_{t-1})$. Attention to future tokens ($j > i$) is strictly forbidden via an upper-triangular $-\infty$ mask.

Causal Barrier

Strictly Left-to-Right

Next Predicted Token

" flowing" (68.4%)

Core Architectural & Practical Divergences

Dimension	BERT (Encoder-only)	GPT (Decoder-only)
Attention Visibility	Bidirectional (All-to-all): Token at position $i$ attends simultaneously to positions $1 \dots N$. Can look ahead into future words.	Causal / Autoregressive: Token at position $i$ only attends to positions $j \le i$. Upper triangle is hard-masked with $-\infty$.
Self-Attention Mask	Identity / Unmasked: full dense $N \times N$ matrix.	Lower Triangular: $M_{i,j} = 0$ if $j \le i$, else $-\infty$.
Pre-training Objective	Masked Language Modeling (MLM) predicting 15% missing tokens + Next Sentence Prediction (NSP).	Standard Next-Token Autoregressive Loss: $\max \sum_t \log P(x_t \mid x_{
Downstream Usage	Requires an auxiliary task head (e.g., linear classification head for sentiment or token classification for NER). Fine-tuned end-to-end.	Few-shot in-context learning, prompt engineering, and open-ended text completion without structural modifications.
Primary Strengths	Semantic search embedding, re-ranking, named entity recognition (NER), extractive question answering (e.g. SQuAD).	Generative conversations, coding, creative writing, step-by-step reasoning, mathematical problem solving.

Interactive Scenario Selector: Which model should you use?

Vector Search & Document Re-ranking

Produce dense embedding vectors representing an entire document or passage for cosine similarity lookups.

✓ BERT / Bi-Encoder Architecture

Multi-Turn Chatbot & Agent Reasoning

Generate coherent, multi-paragraph responses and follow instructions step-by-step.

✓ GPT / Causal Decoder Architecture

Token-Level Named Entity Recognition (NER)

Label individual words (Person, Organization, Location) where future tokens in the sentence resolve ambiguities.

✓ BERT / Token Classification Head

Code Generation & Autocomplete

Stream code token-by-token based on previous function signatures and comments.

✓ GPT / Autoregressive Sampling