BERT vs. GPT
BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are both representative language models based on the Transformer. Both were published in the same year (2018), but their architectural design philosophies are opposites, and the tasks they excel at differ. Understanding which type of model is being used helps you accurately grasp the characteristics and appropriate use of AI tools.
Target audience: Those who understand the basics of the Transformer (Self-Attention, Encoder/Decoder structure).
Estimated learning time: 20 minutes to read
Prerequisites: Must have read Transformer Models
The Difference in Design Philosophy Between BERT and GPT
Section titled “The Difference in Design Philosophy Between BERT and GPT”Both BERT and GPT are based on the Transformer, but since their purposes differ, the architectures and training methods they use are contrasting.
graph LR
subgraph BERT["BERT (Encoder-Only)\nUnderstands context bidirectionally"]
B1["[CLS] Yesterday"] --> B2["in Tokyo"] --> B3["[MASK]"] --> B4["I ate"]
B3 -.->|"References context from both directions"| B1
B3 -.-> B4
end
subgraph GPT["GPT (Decoder-Only)\nGenerates text left to right"]
G1["Yesterday"] --> G2["in Tokyo"] --> G3["ramen"] --> G4["I ate"]
G4 -.->|"References only past tokens"| G1
endBERT (Bidirectional Encoder Representations from Transformers)
Section titled “BERT (Bidirectional Encoder Representations from Transformers)”BERT is an Encoder-Only language model published by Google in 2018.
Key Features
Section titled “Key Features”Bidirectional Encoder: When processing each token, BERT simultaneously references context from both the left and right of that token. When determining whether “bank” in “He went to the bank” refers to a financial institution or a riverbank, it can look at words both before and after to decide.
Pre-training Methods
Section titled “Pre-training Methods”BERT uses two tasks for pre-training.
Masked Language Model (MLM): Some tokens (15%) in the input sentence are randomly replaced with [MASK], and the model predicts what the masked token was. Because prediction requires gathering information from both before and after in the sentence, bidirectional contextual understanding is developed.
Input: "I [MASK] ramen yesterday"
Prediction: "ate", "ordered", "had"...Next Sentence Prediction (NSP): Given two sentences, the model predicts whether the second sentence follows the first. This trains the model to understand logical relationships between sentences.
Tasks BERT Excels At
Section titled “Tasks BERT Excels At”| Task | Description | Example |
|---|---|---|
| Text classification | Determine a text’s category | Positive/negative sentiment analysis |
| Named Entity Recognition (NER) | Identify person names, place names, etc. in text | ”Tanaka lives in Tokyo” |
| Extractive QA | Extract the answer location from a passage | Finding specific information in a document |
| Sentence similarity | Determine how similar two sentences are | Duplicate content detection, improving search accuracy |
GPT (Generative Pre-trained Transformer)
Section titled “GPT (Generative Pre-trained Transformer)”GPT is a Decoder-Only language model published by OpenAI in 2018. It has continued to evolve through GPT-2 (2019), GPT-3 (2020), and GPT-4 (2023).
Key Features
Section titled “Key Features”Unidirectional Decoder: When generating each token, GPT references only the tokens that appear before (to the left of) that token. This is called autoregressive. Since the structure uses only past context to predict the next token, it’s well-suited for text generation.
Pre-training Method
Section titled “Pre-training Method”Next-token prediction (Causal Language Modeling): The model is trained on the task of predicting the next token in a given text.
Input: "Yesterday, in Tokyo"
Prediction: "I had ramen", "I met a friend", "there was a meeting"...Repeating this training on large amounts of text data gives the model the ability to generate natural sentences.
Tasks GPT Excels At
Section titled “Tasks GPT Excels At”| Task | Description | Example |
|---|---|---|
| Text generation | Generate natural text following an input | Automatic writing of emails, articles, code |
| Dialogue / chat | Continue a conversation while maintaining context | ChatGPT, Claude and other conversational AIs |
| Summarization | Condense long text | Summarizing meeting notes or papers |
| Translation | Convert text to another language | Creating multilingual content |
| Code generation | Generate code from natural language instructions | Development assistance like GitHub Copilot |
Detailed Comparison Table: BERT vs. GPT
Section titled “Detailed Comparison Table: BERT vs. GPT”| Comparison | BERT | GPT (GPT-3 and later) |
|---|---|---|
| Developer | Google (2018) | OpenAI (2018–) |
| Architecture | Encoder-Only | Decoder-Only |
| Context direction | Bidirectional (both left and right) | Unidirectional (left only) |
| Training method | MLM + NSP | Next-token prediction |
| Best tasks | Text understanding, classification, NER | Text generation, dialogue |
| Main applications | Search, sentiment analysis, information extraction | Chatbots, code generation |
| Representative models | BERT, RoBERTa, ALBERT | GPT-3/4, Claude, Llama |
| Parameter scale | 110M–340M (base models) | Tens of billions to hundreds of billions |
Derivative Models and Their Positioning
Section titled “Derivative Models and Their Positioning”Following BERT and GPT, many derivative and successor models were developed.
Key BERT Derivatives
Section titled “Key BERT Derivatives”| Model | Developer | Features |
|---|---|---|
| RoBERTa | Meta AI (2019) | Improved BERT. Removes NSP and trains on more data |
| ALBERT | Google (2019) | Reduces BERT’s parameters for a lighter footprint |
| DistilBERT | Hugging Face (2019) | BERT compressed via knowledge distillation. 2× faster, 40% smaller |
| ELMo | Allen Institute (2018) | Bidirectional model before BERT. LSTM-based |
Key GPT Derivatives and Competitors
Section titled “Key GPT Derivatives and Competitors”| Model | Developer | Features |
|---|---|---|
| GPT-2 | OpenAI (2019) | Text generation capability first widely noticed |
| GPT-3 | OpenAI (2020) | 175B parameters. Demonstrates few-shot learning ability |
| GPT-4 | OpenAI (2023) | Multimodal support. Can understand and process images |
| Llama 2/3 | Meta AI (2023–2024) | Open-source Decoder-Only model |
| Claude | Anthropic (2023–) | Safety-focused design. Strong at long-form processing |
Encoder-Decoder Models (Using Both)
Section titled “Encoder-Decoder Models (Using Both)”| Model | Developer | Features |
|---|---|---|
| T5 | Google (2020) | Processes all tasks as Text-to-Text |
| BART | Meta AI (2019) | Strong at summarization and translation |
Practical Usage Guide
Section titled “Practical Usage Guide”graph TD
Task["What kind of task?"]
Task -->|"Analyze / classify existing text"| BERT_Use["Use BERT-series\nSentiment analysis · NER · Search"]
Task -->|"Generate / have dialogue"| GPT_Use["Use GPT-series\nChatGPT · Claude · Copilot"]
Task -->|"Translation / summarization (both long input and output)"| T5_Use["Use Encoder-Decoder\nT5 · BART"]When BERT-series is appropriate:
- Wanting to classify large amounts of reviews/feedback as positive or negative
- Wanting to automatically extract assignee names, dates, and case numbers from customer emails
- Wanting to improve semantic search accuracy over internal documents
When GPT-series is appropriate:
- Wanting to build a conversational customer support system
- Wanting to automatically generate text or code based on user instructions
- Wanting to summarize, translate, or convert existing content to a different format
Summary
Section titled “Summary”- BERT is Encoder-Only and understands context bidirectionally, excelling at text analysis and classification tasks
- GPT is Decoder-Only and autoregressively generates text, excelling at generation and dialogue tasks
- Both are based on the Transformer, but their purposes and designs are opposite
- In practice, the basic rule is: “analysis/classification” → BERT-series, “generation/dialogue” → GPT-series
Frequently Asked Questions
Section titled “Frequently Asked Questions”Q: Is ChatGPT the same as GPT?
A: No. GPT is a language model (foundation model) developed by OpenAI. ChatGPT is an application specialized for dialogue, built on GPT and fine-tuned with reinforcement learning from human feedback (RLHF). GPT is the “engine”; ChatGPT is the “dialogue product” built using that engine.
Q: Is BERT still used today?
A: Yes. It’s particularly widely used for text understanding tasks — search engines (Google has adopted BERT to improve search quality), corporate document classification and information extraction, and sentiment analysis. However, as large-scale generative models (like GPT-4) have emerged, some tasks previously suited for BERT are increasingly being replaced by GPT-series models.
Q: Which model is “smarter”?
A: “Smartness” varies by task. For text classification and information extraction accuracy, BERT-series can still be superior in some cases. For conversational naturalness and text generation quality, modern GPT-series models (GPT-4, Claude, etc.) are dramatically superior. Choosing the right model for the use case is important.
Q: Is Llama BERT-series or GPT-series?
A: Llama (Meta AI) adopts a Decoder-Only architecture, so it’s classified as GPT-series. It’s published as open-source and widely used, fine-tuned for various purposes.
Next step: Reasoning Models