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EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions

Xiping Liu, Zhao Tan

TL;DR

The paper tackles Text-to-SQL by introducing Error-Prevention Instructions (EPIs) that are derived from error-prone instances and tailored to the current task. It proposes EPI-SQL, a zero-shot framework that builds EPIs through error-prone instance collection, general EPIs, contextualized EPIs, and SQL generation with EPIs, and integrates them into the prompt without demonstrations. On Spider, the approach with GPT-4 reaches 85.1% execution accuracy and 77.9% test-suite accuracy, rivaling advanced few-shot methods and surpassing several zero-shot baselines. Ablation studies show that EPI-verification and question similarity are crucial to performance, while biases in data and schema influence error patterns. Overall, the work demonstrates that task-specific, contextualized instructions can substantially boost LLM-based NLP tasks and suggests avenues for broader application of instruction-based enhancements.

Abstract

The conversion of natural language queries into SQL queries, known as Text-to-SQL, is a critical yet challenging task. This paper introduces EPI-SQL, a novel methodological framework leveraging Large Language Models (LLMs) to enhance the performance of Text-to-SQL tasks. EPI-SQL operates through a four-step process. Initially, the method involves gathering instances from the Spider dataset on which LLMs are prone to failure. These instances are then utilized to generate general error-prevention instructions (EPIs). Subsequently, LLMs craft contextualized EPIs tailored to the specific context of the current task. Finally, these context-specific EPIs are incorporated into the prompt used for SQL generation. EPI-SQL is distinguished in that it provides task-specific guidance, enabling the model to circumvent potential errors for the task at hand. Notably, the methodology rivals the performance of advanced few-shot methods despite being a zero-shot approach. An empirical assessment using the Spider benchmark reveals that EPI-SQL achieves an execution accuracy of 85.1\%, underscoring its effectiveness in generating accurate SQL queries through LLMs. The findings indicate a promising direction for future research, i.e. enhancing instructions with task-specific and contextualized rules, for boosting LLMs' performance in NLP tasks.

EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions

TL;DR

The paper tackles Text-to-SQL by introducing Error-Prevention Instructions (EPIs) that are derived from error-prone instances and tailored to the current task. It proposes EPI-SQL, a zero-shot framework that builds EPIs through error-prone instance collection, general EPIs, contextualized EPIs, and SQL generation with EPIs, and integrates them into the prompt without demonstrations. On Spider, the approach with GPT-4 reaches 85.1% execution accuracy and 77.9% test-suite accuracy, rivaling advanced few-shot methods and surpassing several zero-shot baselines. Ablation studies show that EPI-verification and question similarity are crucial to performance, while biases in data and schema influence error patterns. Overall, the work demonstrates that task-specific, contextualized instructions can substantially boost LLM-based NLP tasks and suggests avenues for broader application of instruction-based enhancements.

Abstract

The conversion of natural language queries into SQL queries, known as Text-to-SQL, is a critical yet challenging task. This paper introduces EPI-SQL, a novel methodological framework leveraging Large Language Models (LLMs) to enhance the performance of Text-to-SQL tasks. EPI-SQL operates through a four-step process. Initially, the method involves gathering instances from the Spider dataset on which LLMs are prone to failure. These instances are then utilized to generate general error-prevention instructions (EPIs). Subsequently, LLMs craft contextualized EPIs tailored to the specific context of the current task. Finally, these context-specific EPIs are incorporated into the prompt used for SQL generation. EPI-SQL is distinguished in that it provides task-specific guidance, enabling the model to circumvent potential errors for the task at hand. Notably, the methodology rivals the performance of advanced few-shot methods despite being a zero-shot approach. An empirical assessment using the Spider benchmark reveals that EPI-SQL achieves an execution accuracy of 85.1\%, underscoring its effectiveness in generating accurate SQL queries through LLMs. The findings indicate a promising direction for future research, i.e. enhancing instructions with task-specific and contextualized rules, for boosting LLMs' performance in NLP tasks.
Paper Structure (23 sections, 1 equation, 8 figures, 2 tables)

This paper contains 23 sections, 1 equation, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Problem Description
  4. Overview of Our Method
  5. Error-prone Instances Collection
  6. General EPIs Generation
  7. Contextualized EPI Generation
  8. SQL Generation using EPI
  9. Experiments
  10. Experiment Settings
  11. Main Result
  12. Ablation Study
  13. Text-to-SQL Biases Analysis
  14. Related Work
  15. Conclusion
  16. ...and 8 more sections

Figures (8)

  • Figure 1: An example of EPI and answers generated by EPI-SQL. The orange line represents the connection of EPI and potential errors, and the green line indicates the connection of correct answer and EPI.
  • Figure 2: The framework of our method.
  • Figure 3: Examples of the input and output of prompts used to construct the QSESet.
  • Figure 4: Examples of the input and output of prompts used to generate contextualized EPI and EPI-SQL.
  • Figure 5: Error distribution and error rate for each question cluster, where cluster = 20,60,100.
  • ...and 3 more figures