Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG

Jisoo Jang, Tien-Cuong Bui, Yunjun Choi, Wen-Syan Li

TL;DR

The paper tackles NL-to-SQL conversion by introducing Error Correction through Prompt Tuning (ECPT), which combines diagnosis-based prompts, embedding-fine-tuned retrieval, and Retrieval-Augmented Generation to repair SQL errors. Central to ECPT is a three-step process (Diagnose, Write Prescription, Apply Treatment) supported by a fine-tuned Sentence Transformer to organize correction cases and FAISS-based retrieval, enabling targeted corrections guided by external knowledge. Evaluation on the Spider dev set using GPT-3.5-turbo and GPT-4-turbo shows a substantial improvement, achieving up to about $88.18\%$ execution accuracy with full ECPT, a roughly 12% gain over baselines. The work demonstrates the value of integrating error-diagnosis, embedding-focused retrieval, and external knowledge sources to enhance NLIDB reliability, while highlighting avenues for future improvements such as human-in-the-loop checks and cost-aware RAG deployment.

Abstract

This paper introduces an Error Correction through Prompt Tuning for NL-to-SQL, leveraging the latest advancements in generative pre-training-based LLMs and RAG. Our work addresses the crucial need for efficient and accurate translation of natural language queries into SQL expressions in various settings with the growing use of natural language interfaces. We explore the evolution of NLIDBs from early rule-based systems to advanced neural network-driven approaches. Drawing inspiration from the medical diagnostic process, we propose a novel framework integrating an error correction mechanism that diagnoses error types, identifies their causes, provides fixing instructions, and applies these corrections to SQL queries. This approach is further enriched by embedding fine-tuning and RAG, which harnesses external knowledge bases for improved accuracy and transparency. Through comprehensive experiments, we demonstrate that our framework achieves a significant 12 percent accuracy improvement over existing baselines, highlighting its potential to revolutionize data access and handling in contemporary data-driven environments.

Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG

TL;DR

The paper tackles NL-to-SQL conversion by introducing Error Correction through Prompt Tuning (ECPT), which combines diagnosis-based prompts, embedding-fine-tuned retrieval, and Retrieval-Augmented Generation to repair SQL errors. Central to ECPT is a three-step process (Diagnose, Write Prescription, Apply Treatment) supported by a fine-tuned Sentence Transformer to organize correction cases and FAISS-based retrieval, enabling targeted corrections guided by external knowledge. Evaluation on the Spider dev set using GPT-3.5-turbo and GPT-4-turbo shows a substantial improvement, achieving up to about execution accuracy with full ECPT, a roughly 12% gain over baselines. The work demonstrates the value of integrating error-diagnosis, embedding-focused retrieval, and external knowledge sources to enhance NLIDB reliability, while highlighting avenues for future improvements such as human-in-the-loop checks and cost-aware RAG deployment.

Abstract

This paper introduces an Error Correction through Prompt Tuning for NL-to-SQL, leveraging the latest advancements in generative pre-training-based LLMs and RAG. Our work addresses the crucial need for efficient and accurate translation of natural language queries into SQL expressions in various settings with the growing use of natural language interfaces. We explore the evolution of NLIDBs from early rule-based systems to advanced neural network-driven approaches. Drawing inspiration from the medical diagnostic process, we propose a novel framework integrating an error correction mechanism that diagnoses error types, identifies their causes, provides fixing instructions, and applies these corrections to SQL queries. This approach is further enriched by embedding fine-tuning and RAG, which harnesses external knowledge bases for improved accuracy and transparency. Through comprehensive experiments, we demonstrate that our framework achieves a significant 12 percent accuracy improvement over existing baselines, highlighting its potential to revolutionize data access and handling in contemporary data-driven environments.

Paper Structure

This paper contains 15 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Large language model (LLM)
  4. Fine-tuning Language Models
  5. Retrieval-Augmented Generation
  6. Error Analysis
  7. Methodology
  8. Terminology Definitions
  9. Embedding Fine-Tuning
  10. Experiments and Results
  11. Evaluations, Models, and Metrics
  12. Embedding Fine-Tuning
  13. Results on Execution Accuracy
  14. Hit Rate and Cost Usage
  15. Conclusion

Figures (5)

  • Figure 1: Overview of Error Correction through Prompt Tuning (ECPT)
  • Figure 2: Statistics of error classification
  • Figure 3: Diagnosis Prompt and Prescription Prompt are designed to diagnose and write prescriptions. Red blocks refer to the new error case information, yellow blocks are error type information, blue blocks are relevant case examples, and green blocks are answers that LLM should fill in.
  • Figure 4: Embedding vector spaces of correction cases visualized via T-SNEvan2008visualizing. Each point represents an embedded vector for each correction case. Success cases are colored green, and other colors are failed cases.
  • Figure 5: Experiments results. Percentage values in the x-axis mean accuracies of different configurations with LLM models.