Text-to-SQL

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Text-to-SQL studies how to turn natural-language questions into database queries that retrieve the intended information. Systems link questions to tables, columns, and values, then generate SQL using language models, structured intermediate plans, retrieval, or schema-aware semantic metadata. Common approaches add query decomposition, execution feedback, verification, and iterative repair; training methods include fine-tuning on curated or synthetic examples and reinforcement learning with execution-based rewards. Benchmarks such as Spider and BIRD test execution accuracy, but real deployments also expose challenges in large or changing schemas, ambiguity, multiple SQL dialects, and balancing reliability against cost and latency.

Latest papers

173 papers24 papers in the last four weeks, up 33% on the four weeks before. 0.1% of all new papers.

Schema-Free Text-to-SQL: Learning Enterprise Database Structure from Query Logs
Oct 24, 2026Ömer Faruk Akgül, Nikhil Kapahi, Prajit Reddy MuppidiText-to-SQL Graph Retrieval-Augmented Generation
University of Southern California · Amazon
BIRD-RL: Scaling Agentic Reinforcement Learning over Stateful Data-Centric Environments
Oct 21, 2026Ge Qu, Jinyang Li, Xiaohan Xu +6Agentic RL RL for LLM Agents
The University of Hong Kong · National University of Singapore · The Chinese University of Hong Kong, Shenzhen
Tracing Recurrent and Emergent Hallucination Patterns in Decomposed Text-to-SQL Generation
Oct 20, 2026Hayden Moore, Abhinav Verma, Anshuman DuttHallucination in Language Models Text-to-SQL
The Pennsylvania State University · Microsoft
Probing Ambiguity: Adversarial Intent Resolution for Robust Text-to-SQL
Oct 16, 2026Youbing Yin, Xujun Peng, Muhammad S. Uddin +3Text-to-SQL Execution-Guided Text-to-SQL
Capital One
NL2SQL with Iterative Self-Refinement
Oct 16, 2026Anjali Vemula, Akshat DasulaText-to-SQL Execution-Guided Text-to-SQL
Department of CSE, University of Minnesota - Twin Cities, Minneapolis, MN 55455
PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems
Oct 15, 2026Shuo Lin, Jinzhe Li, Chenlu Guo +3Text-to-SQL Text-to-SQL Evaluation
School of Artificial Intelligence, Jilin University · International Center of Future Science, Jilin University · Bilibili Inc. +2
VeriPIE: Checkable Query Plans and Evidence-Guided Selection for Text-to-SQL
Oct 12, 2026Piyush Jha, Cruise Song, Zhengyu Li +1Execution-Guided Text-to-SQL Text-to-SQL
Georgia Tech
Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows
Oct 12, 2026Bilel Abderrahmane Benziane, Xavier ScheplerText-to-SQL Small Language Models
Data Lab, Generix Group, Paris, France
Does a Governed Confidence Score Know When a Text-to-SQL Answer Is Right? An Exploratory Study on BIRD and a Pre-registered Confirmation on Spider
Oct 12, 2026Raghu Venkat, Tricha AnjaliConfidence Estimation in Language Models Selective Prediction
Numberz.ai Inc., Alpharetta, Georgia, USA
LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement
Oct 10, 2026Ruichen Xia, Xiao Han, Dingyu Yang +5LLM-Based Program Synthesis Text-to-SQL
The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China · Institute of Computing Innovation, Zhejiang University, Hangzhou, China · Zhejiang Geely Holding Group Co., Ltd., Hangzhou, China
DSABench: Benchmarking Source Discovery, Coordination, and Execution for Multi-Database Agents
Oct 10, 2026Jinlong Luo, Haoran Wu, Bo XuAgent Benchmarks Text-to-SQL
Institute of Automation, Chinese Academy of Sciences, Beijing, China
HarnessSQL: Harness-Native Training for SQL Agents in Realistic Database Environments
Oct 8, 2026Haolin Yang, Jipeng Zhang, Zhiqiang Qian +4Text-to-SQL LLM Agent Training
Hong Kong University of Science and Technology · Microsoft Research · Tsinghua University +1
Conversational Task Disambiguation over Tabular Data: Leakage-Aware Formulation, Benchmark Suite, and Training
Oct 7, 2026Nafiseh Ghoroghchian, Luis Scoccola, Tina Sedaghat +4Task-Oriented Dialogue Systems Text-to-SQL
Layer 6 AI
Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation
Oct 7, 2026Bhawani Shankar Leelar, Pawan Chorasiya, Davin Hill +2CoT Reasoning LLM Code Generation
Optum AI
ARCS: Towards Precise Text-to-SQL via Structured Disambiguation
Oct 7, 2026Yihao Hu, Yanlin Feng, Naoki Otani +1Text-to-SQL
αDuke University · βMegagon Labs
SALUS: Automated Auditing of NL-to-SQL Benchmarks through Weak Supervision of Multi-Agent Output
Oct 4, 2026Shiyuan Zhou, Ashwin Gerard Colaco, Sainyam Galhotra +1Benchmark Auditing Text-to-SQL
University of California, Irvine, Irvine, California, USA · Cornell University, Ithaca, New York, USA
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
Oct 4, 2026Suchan Lee, Woomin Song, Hwanjo Yu +1LLM Self-Correction Execution-Guided Code Generation
POSTECH · KAIST
Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agents
Oct 2, 2026Mincheol Daniel Song, Joshua Ward, Jake Jung +1LLM Agent Reliability Selective Prediction
University of California, Los Angeles
A Governed Agent Architecture for Natural-Language Dashboard Generation over Enterprise DataBrain
Oct 2, 2026Yiou Wu, Zezhi Tang, Ningwei Bai +1Data Analysis Agents LLM Agent Orchestration
Department of Computer Science, University College London, London, United Kingdom
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
Oct 2, 2026Darian Lee, Shannon Rumsey, Jack St. Clair +3Synthetic Data Generation Text-to-SQL
University of California, Santa Cruz 1156 High Street, Santa Cruz, CA 95064 · Adobe
From Benchmarks to Production: A Text-to-SQL System for Complex Financial Data
Oct 2, 2026Arijit Sehanobish, Bruno Gomes Coelho, Guillaume Michel +3Text-to-SQL Financial QA
Kensho Technologies
Beyond Correctness: Resolving Underspecification in Agentic Text-to-SQL
Oct 2, 2026Wen-Zhi Li, Yue Gong, Konstantinos Kanellis +1Text-to-SQL Clarification Question Generation
Department of Computer Science Cornell University · Amazon Web Services
DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis
Oct 2, 2026Raquib Bin Yousuf, Harith Laxman, Vitaliy Shkremetko +10Data Analysis Agents Text-to-SQL
Virginia Tech, VA, USA. · The Chronicle of Higher Education, Washington, DC, USA
Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation
Sep 28, 2026Erfan D. Dehkalani, Seetha Shankaran, Abbot R. Laptook +3Multi-Agent LLM Systems LLM Grounding
Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, and Harvard Medical School, Boston, MA, USA · Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA · Department of Pediatrics, Women & Infants Hospital of Rhode Island, and Warren Alpert Medical School of Brown University, Providence, RI, USA +1
Closing the Cross-Dialect Gap: Query Plans as a Portable Interface in Text-to-SQL
Sep 27, 2026Corentin Royer, Robin Oester, Yotam Perlitz +3Text-to-SQL Relational Databases
IBM Research, Zurich, Switzerland · ETH Zurich, Zurich, Switzerland
REALMS: An AI-Assistant Conversational System for Real-Time Exact Audience Sizing over High-Dimensional Nested Profiles
Sep 24, 2026Haixu Ma, Aditya Bansal, Shubham Lohiya +1Text-to-SQL
Adobe Inc. San Jose, USA
Certified Against Which Oracle? Execution Labels Set the Reported Risk of Conformal Abstention for Text-to-SQL
Sep 22, 2026Jiamiao Liu, Dewen Qiao, Yu Zhang +1Probability Calibration Benchmark Auditing
Department of Information, Xinqiao Hospital Army Medical University (Third Military Medical University) Chongqing 400037, China
LIMIT: Less Is More for Instruction Tuning in Text-to-SQL
Sep 21, 2026Haoyuan Ma, Hengwei Liu, Linjuan Wu +2Text-to-SQL Instruction-Tuning Data Selection
Zhejiang University
An Iterative LangGraph Agent for Text-to-SQL: Natural Language Access to the Chicago Crime Database
Sep 19, 2026Vigneshwar Ravi Rao, Rupesh Swarnakar, Fayeq Jeelani Syed†Text-to-SQL Tool-Using Language Agents
Luddy School of Informatics, Computing and Engineering, Indiana University, Indianapolis, Indiana, USA
The Stochastic Shift: A New Evaluation Paradigm for Text-to-SQL with AI Operators
Sep 17, 2026Tarfah Alrashed, Fatma Ozcan, Per Jacobsson +2Text-to-SQL Evaluation Text-to-SQL
Google

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Schema-Free Text-to-SQL: Learning Enterprise Database Structure from Query Logs

Ömer Faruk Akgül, Nikhil Kapahi, Prajit Reddy Muppidi

University of Southern California · Amazon

Text-to-SQL Graph Retrieval-Augmented Generation

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Text-to-SQL systems achieve remarkable accuracy on academic benchmarks, yet performance drops dramatically in enterprise deployments. This gap stems from a fundamental assumption: existing approaches require complete schema documentation, but enterprise databases typically lack comprehensive documentation, featuring cryptic naming and implicit relationships. We present the first text-to-SQL framework that operates without schema documentation, instead learning database structure from historical query logs. Query logs encode implicit structural knowledge: which tables are queried together, how they join, and what columns map to business concepts. We construct a knowledge graph from parsed query patterns and introduce entity-driven GraphRAG retrieval that grounds user questions in domain terminology rather than relying solely on embedding similarity. Validated on both an industrial deployment with expert evaluation and the Beaver benchmark, our approach substantially outperforms similarity-based retrieval methods, the dominant industry approach, in both executability and answer quality. On Beaver, we achieve 95.7% execution validity compared to 60.8% for embedding-based retrieval, with expert data engineers preferring our system 70% to 10% on internal deployment. Our work demonstrates that text-to-SQL can operate effectively in schema-ambiguous enterprise environments, enabling natural language database interfaces "in the wild".