{"total":40,"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":"2408.05109","title":"A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?","created":"2024-08-09","cited_by_count":56,"area":null},{"id":"2411.00073","title":"RSL-SQL: Robust Schema Linking in Text-to-SQL Generation","created":"2024-10-31","cited_by_count":52,"area":null},{"id":"2507.04701","title":"XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL","created":"2025-07-07","cited_by_count":40,"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":"2409.16751","title":"E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL","created":"2024-09-25","cited_by_count":35,"area":null},{"id":"2503.18596","title":"LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL","created":"2025-03-24","cited_by_count":29,"area":null},{"id":"2510.05318","title":"BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions","created":"2025-10-06","cited_by_count":19,"area":null},{"id":"2506.18951","title":"SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications","created":"2025-06-23","cited_by_count":16,"area":null},{"id":"2501.09310","title":"Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors","created":"2025-01-16","cited_by_count":14,"area":null},{"id":"2601.08778","title":"Pervasive Annotation Errors Break Text-to-SQL Benchmarks and Leaderboards","created":"2026-01-13","cited_by_count":13,"area":null},{"id":"2409.19014","title":"FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark","created":"2024-09-24","cited_by_count":11,"area":null},{"id":"2504.20799","title":"Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges","created":"2025-04-29","cited_by_count":11,"area":null},{"id":"2508.18190","title":"ST-Raptor: LLM-Powered Semi-Structured Table Question Answering","created":"2025-08-25","cited_by_count":11,"area":null},{"id":"2502.10739","title":"BASE-SQL: A powerful open source Text-To-SQL baseline approach","created":"2025-02-15","cited_by_count":9,"area":null},{"id":"2502.12911","title":"Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation","created":"2025-02-18","cited_by_count":9,"area":null},{"id":"2408.04691","title":"Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performance","created":"2024-08-08","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":"2604.17653","title":"PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents","created":"2026-04-19","cited_by_count":5,"area":null},{"id":"2503.21602","title":"GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise","created":"2025-03-27","cited_by_count":4,"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":"2510.14296","title":"Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL","created":"2025-10-16","cited_by_count":4,"area":null},{"id":"2506.00912","title":"Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages","created":"2025-06-01","cited_by_count":3,"area":null},{"id":"2604.25149","title":"Semantic Layers for Reliable LLM-Powered Data Analytics: A Paired Benchmark of Accuracy and Hallucination Across Three Frontier Models","created":"2026-04-28","cited_by_count":3,"area":null},{"id":"2406.10690","title":"Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL","created":"2024-06-15","cited_by_count":2,"area":null},{"id":"2412.07214","title":"Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models","created":"2024-12-10","cited_by_count":2,"area":null},{"id":"2503.17811","title":"Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models","created":"2025-03-22","cited_by_count":2,"area":null},{"id":"2510.01989","title":"Exploring Database Normalization Effects on SQL Generation","created":"2025-10-02","cited_by_count":2,"area":null},{"id":"2603.03742","title":"ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement","created":"2026-03-04","cited_by_count":2,"area":null},{"id":"2510.09014","title":"LitE-SQL: A Lightweight and Efficient Text-to-SQL Framework with Vector-based Schema Linking and Execution-Guided Self-Correction","created":"2025-10-10","cited_by_count":1,"area":null},{"id":"2604.16493","title":"NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2605.04719","title":"Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL","created":"2026-05-06","cited_by_count":1,"area":null},{"id":"2605.29670","title":"EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQL","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2507.17896","title":"VeriMinder: Mitigating Analytical Vulnerabilities in NL2SQL","created":"2025-07-23","cited_by_count":0,"area":null},{"id":"2509.05899","title":"X-SQL: Expert Schema Linking and Understanding of Text-to-SQL with Multi-LLMs","created":"2025-09-07","cited_by_count":0,"area":null},{"id":"2604.12988","title":"ROSE: An Intent-Centered Evaluation Metric for NL2SQL","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2606.01246","title":"SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2608.11889","title":"DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2609.24186","title":"LIMIT: Less Is More for Instruction Tuning in Text-to-SQL","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2610.14777","title":"LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}