Chat-Driven Network Management with NLP and Optimization
Research Paper#Network Management, NLP, Optimization, LLM🔬 Research|Analyzed: Jan 3, 2026 06:29•
Published: Dec 31, 2025 04:14
•1 min read
•ArXivAnalysis
This paper addresses the limitations of intent-based networking by combining NLP for user intent extraction with optimization techniques for feasible network configuration. The two-stage framework, comprising an Interpreter and an Optimizer, offers a practical approach to managing virtual network services through natural language interaction. The comparison of Sentence-BERT with SVM and LLM-based extractors highlights the trade-off between accuracy, latency, and data requirements, providing valuable insights for real-world deployment.
Key Takeaways
- •Combines NLP for intent extraction with optimization for feasible network configuration.
- •Offers a two-stage framework (Interpreter and Optimizer) for chat-driven network management.
- •Compares Sentence-BERT with SVM and LLM-based intent extractors, highlighting trade-offs.
- •Provides a user-friendly and interpretable approach to virtual network management.
Reference / Citation
View Original"The LLM-based extractor achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation."