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Comprehensive Guide to Large Language Model Engineering ...

_Under construction_🏗️ ...

1. Data Acquisition and Preprocessing

  1. Web Data Collection (crawling, scraping, parsing)
  2. Corpus Creation and Cleaning (deduplication, filtering)
  3. Data Augmentation Techniques (back-translation, word replacements)
  4. Data Labeling and Annotation

2. Model Architectures

  1. Transformer-based Models (attention, positional encoding)
  2. Encoder-Decoder Models (for sequence tasks)
  3. Autoregressive Models (causal, masked language modeling)
  4. Model Scaling and Efficiency (depth, width, pruning, quantization)

3. Training Strategies

  1. Pretraining Objectives (MLM, NSP, replaced token detection)
  2. Finetuning and Transfer Learning
  3. Few-shot and Zero-shot Learning
  4. Optimization (SGD, Adam, learning rate schedules)
  5. Regularization (dropout, weight decay, early stopping)
  6. Distributed and Parallel Training
  7. Strategy to pretrain a model

4. Evaluation and Benchmarking

  1. Language Modeling Metrics (perplexity, cross-entropy)
  2. Task-specific Evaluation (GLUE, SQuAD, summarization)
  3. Human Evaluation (fluency, relevance, creativity)
  4. Bias and Fairness Assessment

5. Deployment and Inference

  1. Model Compression (pruning, quantization, distillation)
  2. Efficient Inference (caching, hardware optimizations)
  3. Serving Infrastructure (APIs, containerization, scalability)
  4. Monitoring and Maintenance

6. Ethical Considerations

  1. Privacy and Data Protection
  2. Bias Mitigation and Fair Representation
  3. Transparency and Explainability
  4. Responsible Development and Deployment

7. Advanced Research Directions

  1. Multimodal Models (text, vision, audio)
  2. Lifelong Learning and Adaptation
  3. Reasoning and Knowledge Integration
  4. Efficient and Sustainable AI

8. Model Analysis and Interpretability

  1. Attention Visualization and Interpretation
  2. Probing and Diagnostic Classifiers
  3. Counterfactual Analysis
  4. Concept Activation Vectors

9. Domain Adaptation and Transfer

  1. Unsupervised Domain Adaptation
  2. Few-shot Domain Adaptation
  3. Cross-lingual Transfer

10. Model Compression and Acceleration

  1. Knowledge Distillation
  2. Quantization and Pruning
  3. Neural Architecture Search

11. Robustness and Security

  1. Adversarial Attacks and Defenses
  2. Out-of-Distribution Detection
  3. Robust Training Techniques

12. Multilinguality

  1. Multilingual Pretraining
  2. Cross-lingual Alignment
  3. Zero-shot Cross-lingual Transfer

13. Dialogue and Conversational AI

  1. Dialogue State Tracking
  2. Response Generation
  3. Dialogue Evaluation

14. Commonsense and Knowledge Integration

  1. Knowledge Graphs and Ontologies
  2. Commonsense Knowledge Bases
  3. Knowledge-Grounded Generation

15. Few-shot Learning

  1. Meta-learning Approaches
  2. Prompt Engineering
  3. Zero-shot Task Generalization

16. Interpretability and Explainability

  1. Feature Attribution
  2. Concept Activation Vectors
  3. Counterfactual Explanations

17. Multimodal and Grounded Learning

  1. Vision-Language Models
  2. Speech-Language Models
  3. Embodied Language Learning

18. Evaluation and Benchmarking

  1. Intrinsic Evaluation Metrics
  2. Extrinsic Evaluation Tasks
  3. Evaluation Frameworks and Platforms

19. Efficient Training and Deployment

  1. Distributed Training Techniques
  2. Hardware Acceleration
  3. Deployment Optimization

20. Lifelong and Continual Learning

  1. Incremental Learning
  2. Meta-learning for Adaptation
  3. Active Learning and Human-in-the-loop

21. Personalization and Customization

  1. User-specific Adaptation
  2. Domain Adaptation and Customization
  3. Controllable Generation

22. Cross-linguality and Multilingual Adaptation

  1. Zero-shot Cross-lingual Transfer
  2. Multilingual Finetuning
  3. Cross-lingual Alignment

23. Responsible AI and Ethics

  1. Fairness and Bias Mitigation
  2. Privacy and Data Protection
  3. Transparency and Accountability

24. Applications and Use Cases

  1. Natural Language Understanding
  2. Natural Language Generation
  3. Information Retrieval and Search

25. Emerging Trends

  1. Reasoning and Knowledge Integration
  2. Multimodal and Grounded Language
  3. Efficient and Sustainable AI

26. Collaborative and Federated Learning

  1. Decentralized Training and Sharing
  2. Incentive Mechanisms
  3. Human Preference Modeling

27. Domain-specific Language Models

  1. Healthcare and Biomedical
  2. Legal and Financial
  3. Education and Assistive Tech

28. Creative and Artistic Applications

  1. Storytelling and Narrative Generation
  2. Poetry and Songwriting
  3. Humor and Joke Generation

29. Social Good and Humanitarian Applications

  1. Crisis Response and Disaster Management
  2. Misinformation Detection and Fact-checking
  3. Mental Health and Wellbeing

30. Community and Knowledge Sharing

  1. Collaboration with Domain Experts
  2. Open Science and Reproducibility
  3. Education and Outreach