arXiv cs.CL· Wajdi Hammami, Irfan Azam, Soumaya Cherkaoui·· 12 小时前AI 评分20
知识引导 LLM 实现天地一体化 6G 网络意图驱动服务编排
Knowledge-Guided LLM for Intent-Driven Service Orchestration in Integrated Terrestrial and Non-Terrestrial 6G Networks
AI 导读
一个知识引导的 LLM 框架通过语法约束语义解析与知识图谱驱动的澄清循环，在天地一体化 6G 网络中生成可编排的服务配置。在 200 条意图数据集上，该框架达到 89% 分类准确率和 95% 策略有效率，平均每请求仅需 0.66 轮澄清。相比 Pure-LLM 基线，它在提升策略有效性与矛盾检测的同时减少了澄清轮次。
正文
Abstract:Natural-language service intents in integrated terrestrial and non-terrestrial (TN/NTN) 6G networks can be incomplete, ambiguous, or inconsistent, making unconstrained LLM generation challenging for reliable network-facing orchestration. This paper proposes a knowledge-guided LLM framework that combines grammar-constrained semantic parsing with a knowledge-graph (KG)-driven clarification loop to produce validated, orchestration-ready service profiles across four representative service classes. 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. Compared with a Pure-LLM baseline, the proposed framework improves policy validity and contradiction detection while requiring fewer clarification turns, demonstrating the value of explicit knowledge-guided reasoning for robust intent processing in integrated TN/NTN 6G orchestration.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.33007 [cs.CL] |
| (or arXiv:2610.33007v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33007 arXiv-issued DOI via DataCite |
Submission history
From: Wajdi Hammami [view email]
[v1]
Wed, 7 Oct 2026 17:13:24 UTC (17 KB)
来源：arXiv cs.CL · arxiv.org