BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are both representative language models based on the Transformer.[1][2][3] Both were published in 2018, but their architectural design philosophies are contrasting, 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.
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)
BERT is an Encoder-Only language model published by Google in 2018.[2]
Key Features
Bidirectional Encoder: When processing each token, BERT uses context from both the left and right of that token.[2]
Pre-training Methods
BERT uses two tasks for pre-training.
Masked Language Model (MLM): The BERT paper describes replacing part of the input with [MASK] tokens and training the model to predict the masked tokens.[2]
Input: "I [MASK] ramen yesterday"
Prediction: "ate", "ordered", "had"...Next Sentence Prediction (NSP): The BERT paper also used a task where the model predicts whether a second sentence follows the first.[2]
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)
GPT is a Decoder-Only language model published by OpenAI in 2018.[3] GPT-3 is a representative later example of scaling the same autoregressive direction.[4]
Key Features
Unidirectional Decoder: When generating each token, GPT uses preceding tokens to predict the next token. This is called autoregressive.[3][4]
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 text datasets gives the model the ability to generate text that follows the input.[3][4]
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
| Comparison | BERT | GPT-family |
|---|---|---|
| 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 examples | BERT, RoBERTa, ALBERT | GPT, GPT-3, and related autoregressive models |
Derivative Models and Their Positioning
Following BERT and GPT, many derivative and successor models were developed.
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) | A smaller BERT-style model created with knowledge distillation |
| ELMo | Allen Institute (2018) | Bidirectional model before BERT. LSTM-based |
Key GPT-Family Derivatives
| Model | Developer | Features |
|---|---|---|
| GPT | OpenAI (2018) | Early model combining unsupervised pre-training and task-specific fine-tuning |
| GPT-2 | OpenAI (2019) | Scaled autoregressive text generation |
| GPT-3 | OpenAI (2020) | 175B-parameter model demonstrating few-shot learning |
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
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
- 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
Q: Is ChatGPT the same as GPT?
A: No. GPT is a language-model family developed by OpenAI.[3][4] ChatGPT is a chat service provided by OpenAI, and its available underlying models can change over time.[5]
Q: Is BERT still used today?
A: BERT-style models remain useful for text classification, named entity recognition, extractive QA, and similar understanding tasks. In practice, compare them with generative models based on task quality, cost, and deployment constraints.
Q: Which model is “smarter”?
A: “Smartness” varies by task. BERT-style models can be appropriate for classification and extraction, while autoregressive generative models can be appropriate for open-ended generation and dialogue. Choosing the right model for the use case is important.
Q: Is Llama BERT-series or GPT-series?
A: Llama-family models are generally treated as decoder-only autoregressive language models, so their design is closer to GPT-style models than to BERT-style models. Check the distributor’s official materials for each specific model.
References
- Ashish Vaswani et al., Attention Is All You Need, June 12, 2017
- Jacob Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, October 11, 2018
- Alec Radford et al., Improving Language Understanding by Generative Pre-Training, 2018
- Tom B. Brown et al., Language Models are Few-Shot Learners, May 28, 2020
- OpenAI, Introducing ChatGPT, November 30, 2022