Data Preparation for LLMs

Data preparation is a crucial step in training large language models. This guide covers the essential processes and techniques for creating high-quality training datasets.

LLM Training Data Collection

• Data Sources
  • Web crawling
  • Academic datasets
  • Books and literature
  • Code repositories
  • Social media content
  • Specialized domain data
• Data Quality Considerations
  • Content diversity
  • Language distribution
  • Domain coverage
  • Licensing and permissions
  • Ethical considerations

Text Cleaning for LLMs

• Basic Cleaning
  • HTML/XML removal
  • Unicode normalization
  • Special character handling
  • Whitespace normalization
  • Duplicate line removal
• Advanced Processing
  • Language detection
  • Content quality scoring
  • Toxic content filtering
  • PII (Personal Identifiable Information) detection
  • Document structure preservation

Data Filtering and Deduplication

• Deduplication Strategies
  • Exact match detection
  • Near-duplicate detection
  • MinHash algorithms
  • Locality Sensitive Hashing (LSH)
  • N-gram based similarity
• Quality Filters
  • Length-based filtering
  • Language quality scores
  • Perplexity filtering
  • Repetition detection
  • Content classifiers

Creating Training Datasets

• Dataset Formation
  • Sampling strategies
  • Data balancing
  • Domain mixing
  • Format standardization
  • Tokenization considerations
• Data Formats
  • JSON/JSONL
  • Parquet
  • Memory-mapped formats
  • Streaming datasets
  • Distributed storage

Dataset Curation and Quality Control

• Quality Metrics
  • Coverage analysis
  • Bias detection
  • Representation fairness
  • Content evaluation
  • Statistical analysis
• Manual Review
  • Sampling methodology
  • Review guidelines
  • Quality assurance
  • Feedback integration
  • Iterative improvement

Dataset Annotation Workflows

• Annotation Types
  • Classification labels
  • Entity tagging
  • Sentiment annotation
  • Quality ratings
  • Content warnings
• Annotation Management
  • Guidelines development
  • Annotator training
  • Quality monitoring
  • Inter-annotator agreement
  • Review processes

Hugging Face Hub Dataset Management

• Dataset Hosting
  • Upload procedures
  • Version control
  • Access management
  • Documentation
  • Community sharing
• Dataset Cards
  • Metadata documentation
  • Usage guidelines
  • Limitations
  • Ethical considerations
  • Citation information

Learning Resources

Data Processing

• Common Crawl Documentation
• Hugging Face Datasets Guide
• Stanford Text Preprocessing Tutorial

Quality Control

• Data Quality for Machine Learning
• Google’s Data Preparation Best Practices

Ethical Considerations

• Data Ethics Framework
• Responsible AI Practices

Tools and Libraries

• datasets Documentation
• Apache Beam
• DVC (Data Version Control)

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Next: Pre-Training Large Language Models