Knowledge-Guided LLM for Intent-Driven Service Orchestration in Integrated Terrestrial and Non-Terrestrial 6G Networks

A knowledge-guided LLM framework is proposed for intent-driven service orchestration in integrated terrestrial and non-terrestrial 6G networks, combining grammar-constrained semantic parsing with a knowledge-graph-driven clarification loop. On a 200-sample intent dataset, the framework achieves 89% classification accuracy and 95% policy validity with an average of 0.66 clarification turns per request, improving on a Pure-LLM baseline.