BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are both pre-trained language models based on Transformer architecture, but they have significant differences in architecture design, training objectives, and application scenarios.
Architecture Differences
BERT
- Uses Transformer Encoder
- Bidirectional attention mechanism: can see both context
- Auto-encoding model
- Suitable for understanding tasks
GPT
- Uses Transformer Decoder
- Unidirectional attention mechanism: can only see previous context (left-to-right)
- Autoregressive model
- Suitable for generation tasks
Training Objectives
BERT's Training Tasks
1. Masked Language Model (MLM)
- Randomly mask 15% of tokens in the input sequence
- Predict the masked tokens
- Example: Input "The [MASK] sat on the mat", predict "cat"
2. Next Sentence Prediction (NSP)
- Given two sentences, determine if the second sentence follows the first
- Helps model understand relationships between sentences
GPT's Training Tasks
1. Causal Language Modeling
- Predict the next token based on previous context
- Standard autoregressive task
- Example: Given "The cat", predict the next word might be "sat"
Application Scenarios
Tasks BERT Excels At
- Text classification (sentiment analysis, topic classification)
- Named Entity Recognition (NER)
- Question answering (extractive)
- Natural language inference
- Semantic similarity calculation
- Sentence pair classification
Tasks GPT Excels At
- Text generation (stories, articles, dialogue)
- Machine translation
- Code generation
- Creative writing
- Dialogue systems
- Text completion
Performance Characteristics
BERT
- Excellent performance on understanding tasks
- Bidirectional context provides richer semantic information
- Suitable for tasks requiring global understanding
- Relatively faster inference speed
GPT
- Excellent performance on generation tasks
- Emergent abilities with large-scale pre-training (In-context Learning)
- Suitable for tasks requiring creativity and coherence
- Performance improves significantly with model scale
Model Variants
BERT Series
- BERT Base: 12 layers, 110M parameters
- BERT Large: 24 layers, 340M parameters
- RoBERTa: Optimized BERT training strategy
- ALBERT: Lightweight BERT with parameter sharing
- DistilBERT: Distilled lightweight BERT
GPT Series
- GPT-1: 12 layers, 117M parameters
- GPT-2: 48 layers, 1.5B parameters
- GPT-3: 96 layers, 175B parameters
- GPT-4: Multimodal, parameter scale undisclosed
- ChatGPT: Dialogue-optimized version based on GPT-3.5/4
Selection Recommendations
Choose BERT When
- Task is classification, tagging, extraction, etc. (understanding tasks)
- Need bidirectional context information
- Limited computational resources (can use lightweight variants)
- Have requirements for inference speed
Choose GPT When
- Task is generation, creation, etc. (generation tasks)
- Need zero-shot or few-shot learning
- Have sufficient computational resources
- Need model with broad knowledge
Latest Developments
Unified Architectures
- T5: Converts all tasks to text-to-text format
- BART: Combines advantages of encoder and decoder
- LLaMA: Open-source large-scale language model
- ChatGLM: Chinese-optimized dialogue model
Multimodal Extensions
- CLIP: Image-text alignment
- DALL-E: Text-to-image generation
- GPT-4V: Multimodal understanding and generation
Practical Recommendations
Fine-tuning Strategies
- BERT: Usually requires task-specific fine-tuning
- GPT: Can use prompt engineering or fine-tuning
Data Preparation
- BERT: Needs labeled data for fine-tuning
- GPT: Can leverage few-shot learning, reducing labeling needs
Evaluation Metrics
- BERT: Accuracy, F1 score, etc.
- GPT: BLEU, ROUGE, human evaluation, etc.