The Quick Guide to Prompt Engineering

15Building Blocks with BERT

Table of Contents

• Getting to Know BERT
• How BERT Handles Prompts
  • • Tokenization
    • Prepending Special Tokens
    • Embeddings Formation
    • Processing through Transformer Layers
    • Masked Language Modeling
    • Generating Output Representations
    • Task-Specific Heads
• Use Cases and Real-World Examples
  • • Sentiment Analysis
    • Named Entity Recognition
    • Question-Answering Systems
    • Text Classification
    • Language Translation
    • Semantic Text Similarity
    • Content Recommendation
    • Search Optimization
    • Chatbots and Conversational Agents
    • Health Care and Medical Analysis
• Limitations of BERT
  • • Computational Intensity
    • Memory Footprint
    • Overfitting on Smaller Datasets
    • Lack of Transparency
    • Bias and Ethical Concerns
    • Token Limitation
    • Dependency on Context
    • Training from Scratch Challenges
    • Generalization
    • Language Limitations
    • Adversarial Vulnerability
• Future Developments in BERT
  • • Optimization for Efficiency
    • Improving Transparency and Interpretability
    • Addressing Biases
    • Task-Specific Variants
    • Cross-Lingual Advancements
    • Incremental Learning and Adaptation
    • Integration with Other Modalities
    • Robustness and Security
    • Domain Adaptation Techniques
    • Better Integration with Downstream Tasks

Getting to Know BERT

BERT, which stands for Bidirectional Encoder Representations from Transformers, marks a transformative shift in the domain of natural language processing (NLP) and has been a cornerstone for numerous state-of-the-art models developed after its inception. Before diving deep into BERT's specifics, ...