Network Self-Configuration based on Fine-Tuned Small Language Models

TL;DR

This paper presents SLM_netconfig, a small language model framework for network configuration that is efficient, accurate, and privacy-preserving, outperforming larger models in translation accuracy and latency.

Contribution

Introduces a fine-tuned small language model approach for network configuration that is resource-efficient, privacy-preserving, and achieves high accuracy in translating natural language intents into configurations.

Findings

SLM_netconfig outperforms LLM-NetCFG in accuracy metrics.

The system significantly reduces translation latency.

Configurations produced are concise and interpretable.

Abstract

As modern networks grow in scale and complexity, manual configuration becomes increasingly inefficient and prone to human error. While intent-driven self-configuration using large language models has shown significant promise, such models remain computationally expensive, resource-intensive, and often raise privacy concerns because they typically rely on external cloud infrastructure. This work introduces SLM_netconfig, a fine-tuned small language model framework that uses an agent-based architecture and parameter-efficient adaptation techniques to translate configuration intents expressed as natural language requirements or questions into syntactically and semantically valid network configurations. The system is trained on a domain-specific dataset generated through a pipeline derived from vendor documentation, ensuring strong alignment with real-world configuration practices. Extensive evaluation shows that SLM_netconfig, when using its question-to-configuration model, achieves higher syntactic accuracy and goal accuracy than LLM-NetCFG while substantially reducing translation latency and producing concise, interpretable configurations. These results demonstrate that fine-tuned small language models, as implemented in SLM_netconfig, can deliver efficient, accurate, and privacy-preserving automated configuration generation entirely on-premise, making them a practical and scalable solution for modern autonomous network configuration.