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An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model

Ziyang Zheng, Haipeng Jing, Canyu Rui, Askar Hamdulla, Dong Wang

TL;DR

This paper designs two roles using the same LLM: an Actor to produce SQL queries and a Critic to evaluate the produced SQL, and demonstrates that the Actor-Critic method consistently improves the performance of T2S, thus serving as a general enhancement approach for T2S conversion.

Abstract

Text-To-SQL (T2S) conversion based on large language models (LLMs) has found a wide range of applications, by leveraging the capabilities of LLMs in interpreting the query intent expressed in natural language. Existing research focuses on suitable representations for data schema and/or questions, task-specific instructions and representative examples, and complicated inference pipelines. All these methods are empirical and task specific, without a theoretical bound on performance. In this paper, we propose a simple, general, and performance guaranteed T2S enhancement approach called Actor-Critic (AC). Specifically, we design two roles using the same LLM: an Actor to produce SQL queries and a Critic to evaluate the produced SQL. If the Critic believes the produced SQL is wrong, it notifies the Actor to reproduce the SQL and perform evaluation again. By this simple iterative process, expected performance can be derived in theory. We conducted extensive experiments on the Spider and related datasets with eleven LLMs, and demonstrated that the Actor-Critic method consistently improves the performance of T2S, thus serving as a general enhancement approach for T2S conversion.

An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model

TL;DR

This paper designs two roles using the same LLM: an Actor to produce SQL queries and a Critic to evaluate the produced SQL, and demonstrates that the Actor-Critic method consistently improves the performance of T2S, thus serving as a general enhancement approach for T2S conversion.

Abstract

Text-To-SQL (T2S) conversion based on large language models (LLMs) has found a wide range of applications, by leveraging the capabilities of LLMs in interpreting the query intent expressed in natural language. Existing research focuses on suitable representations for data schema and/or questions, task-specific instructions and representative examples, and complicated inference pipelines. All these methods are empirical and task specific, without a theoretical bound on performance. In this paper, we propose a simple, general, and performance guaranteed T2S enhancement approach called Actor-Critic (AC). Specifically, we design two roles using the same LLM: an Actor to produce SQL queries and a Critic to evaluate the produced SQL. If the Critic believes the produced SQL is wrong, it notifies the Actor to reproduce the SQL and perform evaluation again. By this simple iterative process, expected performance can be derived in theory. We conducted extensive experiments on the Spider and related datasets with eleven LLMs, and demonstrated that the Actor-Critic method consistently improves the performance of T2S, thus serving as a general enhancement approach for T2S conversion.

Paper Structure

This paper contains 25 sections, 3 theorems, 9 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Text-to-SQL research
  4. Boosting by Verification
  5. Critics for LLMs
  6. Method
  7. Task definition
  8. Implementation of AC-SQL
  9. Performance bound
  10. Analysis
  11. $prob$ with unlimited $z$
  12. $prob$ with limited $z$
  13. $prob$ on $q$-$s$ plane
  14. Experiments
  15. Datasets
  16. ...and 10 more sections

Key Result

Theorem 1

Suppose that the probability that the Actor produces correct SQL statements is $p$, and the FNR and FPR of the Critic are $q$ and $s$ respectively. If the maximum number of AC iterations is $z$, then the expected performance of the AC process, i.e., the probability that the AC-SQL produces correct S

Figures (4)

  • Figure 1: The framework of AC-SQL. (1) The input involves the SQL statements for schema creation and the question in natural language. (2) The Actor takes this input and generates an SQL candidate. (3) The SQL candidate, together with the input to the Actor, is forwarded to the Critic, where the SQL candidate is evaluated by consulting an LLM (LLM Critic) and accessing the database (Execution Critic). (4) If the output of the Critic is False, the Actor is signified to regenerate the SQL candidate; (5) If the output of the Critic is True, the SQL candidate is output.
  • Figure 2: The expected performance $prob$ of the AC approach with different settings of parameters. The legend on the right-hand side of the figure shows the value of ($p$, $q$, $s$).
  • Figure 3: $prob$ values on the $q$-$s$ plane, with $p = 0.25$ and $z = 5$.
  • Figure 4: $prob$ values on the $q$-$s$ plane, with $p = 0.75$ and $z = 5$.

Theorems & Definitions (5)

  • Theorem 1
  • Corollary 1
  • Corollary 2
  • proof
  • proof