{"total":21,"rows":[{"id":"2406.08426","title":"Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL","created":"2024-06-12","cited_by_count":120,"area":null},{"id":"2407.15186","title":"A Survey on Employing Large Language Models for Text-to-SQL Tasks","created":"2024-07-21","cited_by_count":67,"area":null},{"id":"2406.12692","title":"MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL","created":"2024-06-18","cited_by_count":35,"area":null},{"id":"2506.06541","title":"KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes","created":"2025-06-06","cited_by_count":33,"area":null},{"id":"2403.09732","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","created":"2024-03-13","cited_by_count":27,"area":null},{"id":"2411.13154","title":"DMQR-RAG: Diverse Multi-Query Rewriting for RAG","created":"2024-11-20","cited_by_count":27,"area":null},{"id":"2409.12172","title":"You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL","created":"2024-09-18","cited_by_count":11,"area":null},{"id":"2406.14545","title":"Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems","created":"2024-06-20","cited_by_count":6,"area":null},{"id":"2408.08779","title":"DAC: Decomposed Automation Correction for Text-to-SQL","created":"2024-08-16","cited_by_count":6,"area":null},{"id":"2412.06831","title":"TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Models","created":"2024-12-07","cited_by_count":5,"area":null},{"id":"2603.10697","title":"EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolution","created":"2026-03-11","cited_by_count":5,"area":null},{"id":"2505.23838","title":"Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2410.01869","title":"Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement","created":"2024-10-02","cited_by_count":3,"area":null},{"id":"2509.23459","title":"MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction","created":"2025-09-27","cited_by_count":3,"area":null},{"id":"2507.10903","title":"LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning","created":"2025-07-15","cited_by_count":2,"area":null},{"id":"2508.06387","title":"End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation","created":"2025-08-08","cited_by_count":2,"area":null},{"id":"2603.16434","title":"From Natural Language to Executable Option Strategies via Large Language Models","created":"2026-03-17","cited_by_count":1,"area":null},{"id":"2505.18929","title":"Meta-aware Learning in text-to-SQL Large Language Model","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2508.02045","title":"Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2510.02350","title":"LLMSQL: Upgrading WikiSQL for the LLM Era of Text-to-SQL","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2602.18492","title":"Vibe Coding on Trial: Operating Characteristics of Unanimous LLM Juries","created":"2026-02-12","cited_by_count":0,"area":null}],"next_cursor":null}