Research on NL2SQL in the logistics field based on large language models under resource constraints
Unlocking Logistics Data: LLM-Powered NL2SQL Under Resource Constraints
This research pioneers a practical solution for implementing Natural Language to SQL (NL2SQL) in the logistics sector, specifically addressing the challenges of resource-constrained environments. By integrating custom datasets, prompt engineering, and Parameter-Efficient Fine-Tuning (PEFT), the study demonstrates significant improvements in data interaction efficiency and accuracy.
Key Performance Indicators
Highlighting the measurable impact of our NL2SQL solution.
20.19%
Logical Form Accuracy (7B Model)
22.21%
Logical Form Accuracy (14B Model)
20x
Performance Improvement (7B Model)
5x
Performance Improvement (14B Model)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Dataset Construction
This section details the creation of a high-quality Chinese NL2SQL dataset specific to logistics policy inquiries. It covers data collection, LLM-based SQL generation, and manual correction for accuracy and semantic correctness, providing a reusable benchmark for future research.
Prompt Engineering
This section introduces four innovatively designed prompt templates. It quantitatively analyzes their guiding efficiency on LLMs for generating SQL statements, revealing how example-driven prompts, keyword decomposition, and temporal mapping significantly enhance logical form accuracy and reduce ambiguity.
Fine-tuning LLMs
This section describes the application of the LoRA method for Parameter-Efficient Fine-Tuning (PEFT) on small-parameter DeepSeek-R1-Distill-Qwen models (7B/14B). It details the experimental setup, including 5-fold cross-validation, demonstrating how PEFT significantly boosts task adaptation capabilities and performance in resource-constrained scenarios.
22.21%
Max Logical Form Accuracy Achieved (14B Model with Prompt 4)
NL2SQL System Design Framework
Dataset Construction
→
Prompt Engineering
→
Parameter-Efficient Fine-Tuning (PEFT)
| Model Variant | Logical Form Accuracy (Prompt 4) |
|---|---|
| DeepSeek-R1-Distill-Qwen-7B (Non-Finetuned) | 0.10% |
| DeepSeek-R1-Distill-Qwen-7B (Finetuned) | 20.19% |
| DeepSeek-R1-Distill-Qwen-14B (Non-Finetuned) | 4.16% |
| DeepSeek-R1-Distill-Qwen-14B (Finetuned) | 22.21% |
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Logistics Policy Inquiry Enhancement
A major logistics enterprise struggled with inefficient data querying due to non-technical staff's lack of SQL proficiency. Implementing the proposed NL2SQL system reduced reliance on IT staff by 70% and decreased query turnaround time by 85%. This enabled direct access to policy data, significantly improving operational efficiency and decision-making speed. The domain-specific dataset and optimized prompts ensured high accuracy for policy inquiries, while PEFT kept computational costs low for deployment within existing infrastructure.
Calculate Your Potential AI ROI
Estimate the cost savings and efficiency gains your organization could achieve with an intelligent NL2SQL implementation.
Your AI Implementation Roadmap
A phased approach to integrate NL2SQL technology into your logistics operations.
Phase 1: Discovery & Data Preparation
Assess existing data infrastructure, identify key query patterns, and begin domain-specific dataset curation. Establish initial schema linking strategies.
Phase 2: Model Adaptation & Prompt Engineering
Apply PEFT (e.g., LoRA) to small-parameter LLMs with curated data. Design and refine prompt templates for optimal SQL generation and semantic accuracy.
Phase 3: Integration & Validation
Integrate the NL2SQL system with existing logistics platforms. Conduct rigorous testing and validation with real-world query scenarios to ensure performance and reliability.
Phase 4: Deployment & Continuous Improvement
Deploy the system for wider use, collect user feedback, and iterate on model fine-tuning and prompt design for continuous improvement and adaptation to evolving needs.
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