# Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration

arXiv: https://arxiv.org/abs/2404.15869 · HTML: https://mlsanity.com/paper/2404.15869/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems](https://mlsanity.com/paper/2402.18649/) (2024)
- [Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective](https://mlsanity.com/paper/2310.19233/) (2023)
- [Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks](https://mlsanity.com/paper/2403.02238/) (2024)
- [ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?](https://mlsanity.com/paper/2311.16989/) (2023)
- [Intent Assurance using LLMs guided by Intent Drift](https://mlsanity.com/paper/2402.00715/) (2024)
- [Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph](https://mlsanity.com/paper/2308.13534/) (2023)
- [A Modular, End-to-End Next-Generation Network Testbed: Towards a Fully Automated Network Management Platform](https://mlsanity.com/paper/2403.15376/) (2024)

## Built on by (most cited first, 8 of 8)

- [Doing More with Less: A Survey on Routing Strategies for Resource Optimisation in Large Language Model-Based Systems](https://mlsanity.com/paper/2502.00409/) (2025)
- [When to Reason: Semantic Router for vLLM](https://mlsanity.com/paper/2510.08731/) (2025)
- [Exploring How LLMs Capture and Represent Domain-Specific Knowledge](https://mlsanity.com/paper/2504.16871/) (2025)
- [Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model](https://mlsanity.com/paper/2508.06616/) (2025)
- [Chat-Driven Optimal Management for Virtual Network Services](https://mlsanity.com/paper/2512.24614/) (2025)
- [Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes](https://mlsanity.com/paper/2608.22644/) (2026)
- [Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling](https://mlsanity.com/paper/2604.13271/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications, especially in the fields of natural language processing and generative AI. Not limited to text generation applications, these models inherently possess the opportunity to leverage prompt engineering, where the inputs of such models can be appropriately structured to articulate a model's purpose explicitly. A prominent example of this is intent-based networking, an emerging approach for automating and maintaining network operations and management. This paper presents semantic routing to achieve enhanced performance in LLM-assisted intent-based management and orchestration of 5G core networks. This work establishes an end-to-end intent extraction framework and presents a diverse dataset of sample user intents accompanied by a thorough analysis of the effects of encoders and quantization on overall system performance. The results show that using a semantic router improves the accuracy and efficiency of the LLM deployment compared to stand-alone LLMs with prompting architectures.