Demonstration Data Curation
Master the art of curating high-quality demonstration datasets for fine-tuning language models and RLHF training
55 min read•Advanced
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What is Demonstration Data Curation?
Demonstration data curation is the systematic process of collecting, filtering, and organizing high-quality input-output examples for training language models through supervised fine-tuning, instruction tuning, and RLHF. It involves careful selection of diverse, representative examples that teach models desired behaviors while maintaining consistency, quality, and safety standards.
Collection Phase
- • Human-generated demonstrations
- • Synthetic data generation
- • Existing dataset adaptation
- • Crowdsourcing platforms
- • Expert domain knowledge
Quality Assessment
- • Relevance and accuracy scoring
- • Coherence and clarity metrics
- • Instruction-following fidelity
- • Safety and bias detection
- • Factual consistency validation
Diversity Optimization
- • Task type distribution balance
- • Difficulty level stratification
- • Domain coverage analysis
- • Format and style variation
- • Edge case representation
Active Curation
- • Model-guided selection
- • Uncertainty-based sampling
- • Performance gap analysis
- • Iterative improvement cycles
- • Human-in-the-loop validation
Curation Complexity Calculator
Curation Metrics
Estimated Cost:$747
Curation Time:130 hours
Expected Quality:95.0%
Retention Rate:94.0%
Final Dataset Size:9,400
Implementation Examples
Multi-Dimensional Quality Scorer
Quality Assessment System
Active Learning Curator
Active Curation Pipeline
Diversity Optimization Engine
Diversity Maximization System
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