Post
596
Adaptive Classifier: Dynamic Text Classification with Strategic Learning
New text classification system that learns continuously without catastrophic forgetting. Achieved 22.2% robustness improvement on adversarial datasets while maintaining clean data performance.
🎯 THE PROBLEM
Traditional classifiers require complete retraining when adding new classes. Expensive and time-consuming, especially with adversarial users trying to game the system.
🚀 KEY INNOVATIONS
• Hybrid memory-neural architecture (prototype-based + neural adaptation)
• Strategic classification using game theory to predict and defend against manipulation
• Elastic Weight Consolidation prevents catastrophic forgetting
📊 RESULTS
Tested on AI-Secure/adv_glue dataset:
• Clean data: 80.0% → 82.2% (+2.2%)
• Manipulated data: 60.0% → 82.2% (+22.2%)
• Zero performance drop under adversarial attacks
🔬 APPLICATIONS
• Hallucination detection: 80.7% recall for RAG safety
• LLM routing: 26.6% cost optimization improvement
• Content moderation: Robust against gaming attempts
⚙️ USAGE
pip install adaptive-classifier
from adaptive_classifier import AdaptiveClassifier
classifier = AdaptiveClassifier("bert-base-uncased")
classifier.add_examples(texts, labels)
predictions = classifier.predict("New text")
🔗 RESOURCES
Blog: https://huggingface.co/blog/codelion/adaptive-classifier
Code: https://github.com/codelion/adaptive-classifier
Models:
adaptive-classifier
Available models: llm-hallucination-detector, llm-config-optimizer, llm-router
Works with any HuggingFace transformer. Fully open source and production-ready!
New text classification system that learns continuously without catastrophic forgetting. Achieved 22.2% robustness improvement on adversarial datasets while maintaining clean data performance.
🎯 THE PROBLEM
Traditional classifiers require complete retraining when adding new classes. Expensive and time-consuming, especially with adversarial users trying to game the system.
🚀 KEY INNOVATIONS
• Hybrid memory-neural architecture (prototype-based + neural adaptation)
• Strategic classification using game theory to predict and defend against manipulation
• Elastic Weight Consolidation prevents catastrophic forgetting
📊 RESULTS
Tested on AI-Secure/adv_glue dataset:
• Clean data: 80.0% → 82.2% (+2.2%)
• Manipulated data: 60.0% → 82.2% (+22.2%)
• Zero performance drop under adversarial attacks
🔬 APPLICATIONS
• Hallucination detection: 80.7% recall for RAG safety
• LLM routing: 26.6% cost optimization improvement
• Content moderation: Robust against gaming attempts
⚙️ USAGE
pip install adaptive-classifier
from adaptive_classifier import AdaptiveClassifier
classifier = AdaptiveClassifier("bert-base-uncased")
classifier.add_examples(texts, labels)
predictions = classifier.predict("New text")
🔗 RESOURCES
Blog: https://huggingface.co/blog/codelion/adaptive-classifier
Code: https://github.com/codelion/adaptive-classifier
Models:
adaptive-classifier
Available models: llm-hallucination-detector, llm-config-optimizer, llm-router
Works with any HuggingFace transformer. Fully open source and production-ready!
