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Quy tắc huấn luyện (lựa chọn VB huấn luyện) ​

Tổng quan ​

Quy tắc huấn luyện bao gồm fine-tuning ban đầu và continuous learning để mô hình thích ứng với nội dung mới và thay đổi trong kho dữ liệu pháp luật.

🎯 Mục đích ​

  • Domain adaptation: Tối ưu cho legal domain
  • Performance improvement: Cải thiện accuracy
  • Efficiency: Better than training from scratch
  • Customization: Tailor cho specific use cases
  • Adaptation: Thích ứng với legal changes
  • Freshness: Maintain relevance với current laws
  • Incremental learning: Update without full retraining
  • Knowledge retention: Preserve existing knowledge

📊 Process Fine-tuning ​

📊 Process Continuous Learning ​

🛠️ Techniques Fine-tuning ​

Supervised Fine-tuning ​

  • Task-specific data: Labeled examples
  • Instruction tuning: Follow instructions better
  • Parameter efficient: LoRA, adapters
  • Multi-task learning: Multiple objectives

Data Preparation ​

  • Quality filtering: High-quality examples
  • Diversity: Cover various scenarios
  • Balance: Representative distribution
  • Augmentation: Data expansion

Training Strategy ​

  • Learning rate: Lower than pre-training
  • Batch size: Optimize cho hardware
  • Epochs: Prevent overfitting
  • Early stopping: Monitor validation

🛠️ Techniques Continuous Learning ​

Incremental Learning ​

  • Online learning: Continuous model updates
  • Experience replay: Retain old knowledge
  • Regularization: Prevent catastrophic forgetting
  • Dynamic architectures: Growing models

Change Detection ​

  • Content monitoring: Track document updates
  • Semantic drift: Detect meaning changes
  • Impact analysis: Assess effect on model
  • Priority scoring: High-impact changes first

Selective Training ​

  • Targeted updates: Focus on affected knowledge
  • Few-shot learning: Quick adaptation
  • Meta-learning: Learn to learn quickly
  • Transfer learning: Leverage related knowledge

📈 Methods ​

Full Fine-tuning ​

  • All parameters: Update entire model
  • High cost: Large GPU memory
  • Best performance: Maximum adaptation
  • Risk of forgetting: Catastrophic forgetting

Parameter Efficient ​

  • LoRA: Low-rank adaptation
  • Adapters: Small trainable modules
  • Prompt tuning: Soft prompts
  • Prefix tuning: Task-specific prefixes

🔧 Implementation ​

Frameworks ​

  • Hugging Face Transformers: Standard library
  • Axolotl: Optimized training
  • Unsloth: Fast fine-tuning
  • DeepSpeed: Large model training

Infrastructure ​

  • GPU optimization: Mixed precision, gradient checkpointing
  • Distributed training: Multi-GPU, multi-node
  • Memory management: Gradient accumulation
  • Monitoring: Training metrics

Data Pipeline cho Continuous Learning ​

  • Change streams: Real-time content updates
  • Annotation: Label important changes
  • Batching: Group related updates
  • Quality control: Validate training data

Training Strategy cho Updates ​

  • Micro-batching: Frequent small updates
  • Gradient accumulation: Memory-efficient
  • Mixed precision: Faster training
  • Distributed training: Scale updates

📊 Evaluation ​

Metrics ​

  • Task performance: Accuracy, F1, ROUGE
  • Generalization: Out-of-domain performance
  • Robustness: Adversarial examples
  • Efficiency: Inference speed
  • Knowledge retention: Test old knowledge
  • Adaptation speed: Time to learn new content
  • Quality maintenance: Consistent performance
  • Drift detection: Monitor model degradation

Validation ​

  • Cross-validation: Multiple folds
  • Holdout sets: Unseen data
  • A/B testing: Production comparison
  • Human evaluation: Expert review

🚀 Challenges ​

Catastrophic Forgetting ​

  • Knowledge distillation: Transfer old knowledge
  • Replay buffers: Maintain representative samples
  • Elastic weight consolidation: Protect important weights
  • Progressive networks: Separate knowledge domains

Computational Cost ​

  • Efficient updates: Minimize resource usage
  • Selective computation: Update only necessary parts
  • Caching: Reuse computations
  • Prioritization: Focus on high-impact updates

📈 Best Practices ​

Data Management ​

  • Quality over quantity: Curated datasets
  • Domain relevance: Legal-specific content
  • Bias mitigation: Fair representation
  • Privacy: Data anonymization

Training Optimization ​

  • Hyperparameter tuning: Systematic search
  • Regularization: Prevent overfitting
  • Curriculum learning: Easy to hard
  • Model merging: Combine multiple fine-tunes

Change Management ​

  • Version control: Track content và model versions
  • Rollback capability: Revert problematic updates
  • A/B testing: Validate updates safely
  • Gradual rollout: Phased deployment

Quality Assurance ​

  • Automated testing: Regression test suite
  • Human validation: Expert review of changes
  • Performance baselines: Compare against standards
  • Feedback loops: User feedback integration

Quy tắc huấn luyện đảm bảo mô hình AI luôn cập nhật, relevant và hiệu quả cho tác vụ hỗ trợ tra cứu văn bản pháp luật.