Advanced NLP Techniques
Advanced methods and state-of-the-art approaches in Natural Language Processing
Advanced NLP Techniques
This section covers cutting-edge techniques and approaches in Natural Language Processing that represent the current state of the art.
Transformer Architecture
Self-Attention Mechanism
- Multi-head attention
- Positional encoding
- Scaled dot-product attention
- Attention visualization
Transformer Variants
- BERT and RoBERTa
- GPT family
- T5 and BART
- Lightweight transformers
Transfer Learning
Pre-training Strategies
- Masked language modeling
- Next sentence prediction
- Sequence-to-sequence
- Contrastive learning
Fine-tuning Approaches
- Task-specific adaptation
- Few-shot learning
- Zero-shot learning
- Prompt engineering
Large Language Models
Architecture Scaling
- Model parallelism
- Pipeline parallelism
- Distributed training
- Memory optimization
Inference Optimization
- Knowledge distillation
- Quantization
- Pruning
- Efficient inference
Multi-modal Learning
Text and Vision
- Image captioning
- Visual question answering
- Scene understanding
- Cross-modal retrieval
Text and Audio
- Speech recognition
- Text-to-speech
- Audio captioning
- Multi-modal alignment
Few-shot Learning
Meta-learning
- Model-agnostic meta-learning
- Prototypical networks
- Matching networks
- Optimization-based approaches
Prompt Engineering
- Prompt design
- In-context learning
- Chain-of-thought
- Instruction tuning
Interpretability
Model Analysis
- Attention analysis
- Feature attribution
- Probing tasks
- Behavioral testing
Explanation Methods
- LIME and SHAP
- Counterfactual explanations
- Concept attribution
- Layer visualization
Ethical Considerations
Bias Detection
- Data bias
- Model bias
- Output bias
- Mitigation strategies
Fairness
- Fairness metrics
- Debiasing techniques
- Inclusive design
- Ethical guidelines
Best Practices
- Model selection
- Resource optimization
- Evaluation strategies
- Deployment considerations
- Monitoring and maintenance
Implementation Challenges
- Computational resources
- Data requirements
- Model complexity
- Deployment constraints
- Ethical considerations
Tools and Frameworks
- HuggingFace Transformers
- PyTorch Lightning
- DeepSpeed
- Accelerate
- Weights & Biases
Related Topics
- Model Architecture
- Distributed Computing
- Ethics in AI
- MLOps