Data to 7 Oct 2026

The Dawn of Natural Language to SQL: Are We Fully Ready?

arXiv 2406.01265 ↗First posted 3 Jun 2024cs.DBcited by 78

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Abstract (arXiv)

Translating users' natural language questions into SQL queries (i.e., NL2SQL) significantly lowers the barriers to accessing relational databases. The emergence of Large Language Models has introduced a novel paradigm in NL2SQL tasks, enhancing capabilities dramatically. However, this raises a critical question: Are we fully prepared to deploy NL2SQL models in production? To address the posed questions, we present a multi-angle NL2SQL evaluation framework, NL2SQL360, to facilitate the design and test of new NL2SQL methods for researchers. Through NL2SQL360, we conduct a detailed comparison of leading NL2SQL methods across a range of application scenarios, such as different data domains and SQL characteristics, offering valuable insights for selecting the most appropriate NL2SQL methods for specific needs. Moreover, we explore the NL2SQL design space, leveraging NL2SQL360 to automate the identification of an optimal NL2SQL solution tailored to user-specific needs. Specifically, NL2SQL360 identifies an effective NL2SQL method, SuperSQL, distinguished under the Spdier dataset using the execution accuracy metric. Remarkably, SuperSQL achieves competitive performance with execution accuracy of 87% and 62.66% on the Spider and BIRD test sets, respectively.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 38; 2026: 24.

78 papers.

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2024AFlow: Automating Agentic Workflow Generationcited by 395
2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 132
2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
2024Formal Mathematical Reasoning: A New Frontier in AIcited by 80
2024The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Modelscited by 67
2025Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQLcited by 59
2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
2024RSL-SQL: Robust Schema Linking in Text-to-SQL Generationcited by 51
2025XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQLcited by 40
2025Self-Supervised Prompt Optimizationcited by 37
2024E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQLcited by 35

38 more

2025A Survey of Data Agents: Emerging Paradigm or Overstated Hype?cited by 30
2024Are Large Language Models Good Statisticians?cited by 26
2024HAIChart: Human and AI Paired Visualization Systemcited by 26
2025Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarkingcited by 21
2025SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQLcited by 19
2024R-Bot: An LLM-based Query Rewrite Systemcited by 16
2025CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learningcited by 15
2025LLM/Agent-as-Data-Analyst: A Surveycited by 15
2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
2025Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQLcited by 14
2025MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQLcited by 14
2024ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQLcited by 13
2025EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routingcited by 13
2025LEAD: Iterative Data Selection for Efficient LLM Instruction Tuningcited by 13
2025Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generationcited by 9
2025ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQLcited by 8
2025DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoningcited by 8
2026InfoPO: Information-Driven Policy Optimization for User-Centric Agentscited by 8
2025LearNAT: Learning NL2SQL with AST-guided Task Decomposition for Large Language Modelscited by 7
2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMscited by 7
2026APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQLcited by 7
2024DAC: Decomposed Automation Correction for Text-to-SQLcited by 6
2024Towards Optimizing SQL Generation via LLM Routingcited by 5
2025ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understandingcited by 5
2025SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correctioncited by 5
2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
2025SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generationcited by 4
2025A State-of-the-Art SQL Reasoning Model using RLVRcited by 4
2025Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Tradingcited by 4
2024ETM: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Modelscited by 3
2026Text2GraphQuery-Bench: A Text to Graph Query Benchmarkcited by 3
2026DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspacescited by 3
2025MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learningcited by 2
2025CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generationcited by 2
2025Exploring Database Normalization Effects on SQL Generationcited by 2
2026An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Datacited by 2
2026ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamicscited by 2
2024Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Modelscited by 1

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