Text-to-SQL Research Trends

The field of Text-to-SQL is moving towards improving the accuracy and reliability of natural language queries to SQL translations. Researchers are focusing on addressing the semantic mismatch between natural language questions and their corresponding SQL queries, as well as developing more effective methods for confidence estimation and ambiguity resolution. Notable advancements include the integration of model interpretability analysis, execution-guided strategies, and human feedback mechanisms to enhance the performance of Text-to-SQL systems.

Some noteworthy papers in this area include: CRED-SQL, which achieves state-of-the-art performance on large-scale databases by leveraging cluster retrieval and execution description. AmbiSQL, which introduces an interactive system for detecting and resolving query ambiguities, resulting in significant improvements in SQL exact match accuracy. QueryGenie, which provides a transparent and controllable querying experience by enabling users to monitor and guide the LLM-driven query generation process.

Sources

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description

The Interpretability Analysis of the Model Can Bring Improvements to the Text-to-SQL Task

Confidence Estimation for Text-to-SQL in Large Language Models

Ambiguity Resolution with Human Feedback for Code Writing Tasks

QueryGenie: Making LLM-Based Database Querying Transparent and Controllable

AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL

Built with on top of