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An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model (2410.22082v1)

Published 28 Oct 2024 in cs.DB, cs.CL, and cs.HC

Abstract: Text-To-SQL (T2S) conversion based on 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.

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Summary

  • The paper introduces an actor-critic framework that integrates SQL query generation with iterative verification to improve model performance.
  • It achieves significant execution accuracy gains on benchmarks like Spider using models such as LLaMA, Vicuna, and GPT-4o.
  • The approach provides theoretical guarantees and paves the way for extending reinforcement techniques to broader NLP tasks.

An Actor-Critic Approach to Boosting Text-to-SQL LLMs

The paper "An Actor-Critic Approach to Boosting Text-to-SQL LLMs" presents a novel method to enhance the capabilities of Text-to-SQL (T2S) systems powered by LLMs. This research introduces an improvement on T2S tasks using a reinforcement learning-inspired Actor-Critic framework, which is traditionally utilized in reinforcement learning literature to stabilize training and prediction processes.

Overview of Text-to-SQL

Text-to-SQL has increasingly become a pivotal area of research in natural language processing, given its practical significance in allowing non-expert database users to interact with complex database systems via natural language interfaces. Despite the advancements, challenges remain, primarily due to the diversity and sophistication of SQL queries that often require the understanding of nuanced natural language paired with diverse schemas.

Introduction of the Actor-Critic Framework

The authors propose a theoretically grounded approach referred to as "Actor-Critic" (AC), which effectively integrates two roles within a singular LLM framework. The Actor role is responsible for generating candidate SQL queries, while the Critic evaluates the correctness of these queries. The Critic's evaluation feeds back to the Actor iteratively until the Critic deems the candidate SQL satisfactory, or a maximum number of iterations is reached. This framework aims to bring theoretical guarantees to performance enhancement, distinguishing itself from other empirical methods.

Theoretical Insights and Empirical Resilience

The AC-SQL method's theoretical foundation is established primarily around the computational complexity theory, positioning the Critic in a verifier role distinct from the solver role of the Actor. This separation of concerns enables an iterative improvement mechanism reminiscent of human problem-solving processes, whereby solving and verification are articulated independently to optimize performance safely and generally.

Experimentally, the paper validates the AC-SQL approach using extensive tests across widely acknowledged benchmark datasets - Spider, Spider-DK, and Spider-SYN. The results show remarkable improvement in execution accuracy across a diverse set of LLMs including well-established models such as LLaMA, Vicuna, and GPT-4o, reinforcing the generality and effectiveness of this novel method. This consistent enhancement, coupled with significant error rate reduction, underscores the practical applicability of the Actor-Critic methodology in real-world scenarios.

Implications and Future Work

The Actor-Critic framework proposed herein holds substantial implications for both theoretical exploration and practical application within the AI domain. From a practical perspective, this architecture could potentially be extended beyond T2S tasks to other NLP tasks where validation through sparse feedback is advantageous. Theoretically, it opens avenues for a refined understanding of LLM performance dynamics when engaged in complex problem-solving underpinned by verifiable outputs.

Future investigations could explore the integration of this Actor-Critic framework with more sophisticated critics, possibly leveraging more complex and informative feedback loops and exploring distributed Critic systems to further leverage consensus mechanisms in decision-making processes. Additionally, the efficiency and scalability of the proposed approach are vital aspects requiring in-depth examination to ensure its viability and adaptability in diverse application contexts.

In conclusion, this paper provides a foundational step towards enhancing the reliability and efficiency of Text-to-SQL systems using LLMs by employing an Actor-Critic approach, promising a safer path to consistent performance enhancements without the need for task-specific refinements.

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