# Structure-Aware NL-to-SQL for SFC Provisioning via AST-Masking Empowered Language Models

arXiv: https://arxiv.org/abs/2601.17295 · HTML: https://mlsanity.com/paper/2601.17295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Extendable NFV-Integrated Control Method Using Reinforcement Learning](https://mlsanity.com/paper/1912.09022/) (2019)
- [Integrating Language Models for Enhanced Network State Monitoring in DRL-Based SFC Provisioning](https://mlsanity.com/paper/2502.11298/) (2025)
- [LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning](https://mlsanity.com/paper/2507.10903/) (2025)

## Abstract (arXiv)

Effective Service Function Chain (SFC) provisioning requires precise orchestration in dynamic and latency-sensitive networks. Reinforcement Learning (RL) improves adaptability but often ignores structured domain knowledge, which limits generalization and interpretability. Large Language Models (LLMs) address this gap by translating natural language (NL) specifications into executable Structured Query Language (SQL) commands for specification-driven SFC management. Conventional fine-tuning, however, can cause syntactic inconsistencies and produce inefficient queries. To overcome this, we introduce Abstract Syntax Tree (AST)-Masking, a structure-aware fine-tuning method that uses SQL ASTs to assign weights to key components and enforce syntax-aware learning without adding inference overhead. Experiments show that AST-Masking significantly improves SQL generation accuracy across multiple language models. FLAN-T5 reaches an Execution Accuracy (EA) of 99.6%, while Gemma achieves the largest absolute gain from 7.5% to 72.0%. These results confirm the effectiveness of structure-aware fine-tuning in ensuring syntactically correct and efficient SQL generation for interpretable SFC orchestration.