Performance-Guided Knowledge Distillation (PGKD) shrinks LLMs into tiny models while keeping their classification superpowers
https://arxiv.org/abs/2411.05045
🎯 Original Problem:
LLMs excel at text classification but face deployment challenges due to high inference costs and latency. Production environments need faster, cheaper solutions while maintaining LLM-level performance.
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🛠️ Solution in this Paper:
→ Performance-Guided Knowledge Distillation (PGKD) transfers LLM knowledge into smaller task-specific models through active learning
→ Student model performance metrics guide teacher LLM to generate optimized training data
→ Hard negative mining identifies misclassified samples where student was confident
→ Early stopping prevents performance drift and overfitting
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💡 Key Insights from this Paper:
→ PGKD effectiveness increases with dataset complexity and number of classes
→ Validation metrics feedback helps LLM generate better training samples
→ Hard negative samples improve model decision boundaries
→ Performance gains diminish as training data size increases
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📊 Results:
→ Up to 130X faster inference compared to LLMs
→ 25X lower operational costs
→ Accuracy improvement from 0.320 to 0.443 on complex datasets (335 classes)
→ Consistently outperforms base BERT model across all dataset sizes
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