{"total":119,"rows":[{"id":"2304.11015","title":"DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction","created":"2023-04-21","cited_by_count":477,"area":null},{"id":"2308.15363","title":"Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation","created":"2023-08-29","cited_by_count":385,"area":null},{"id":"2305.09645","title":"StructGPT: A General Framework for Large Language Model to Reason over Structured Data","created":"2023-05-16","cited_by_count":343,"area":"lr"},{"id":"2402.03927","title":"Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs","created":"2024-02-06","cited_by_count":229,"area":null},{"id":"2402.16347","title":"CodeS: Towards Building Open-source Language Models for Text-to-SQL","created":"2024-02-26","cited_by_count":187,"area":null},{"id":"2307.07306","title":"C3: Zero-shot Text-to-SQL with ChatGPT","created":"2023-07-14","cited_by_count":164,"area":null},{"id":"2305.18486","title":"A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets","created":"2023-05-29","cited_by_count":123,"area":null},{"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":"2310.06356","title":"A Semantic Invariant Robust Watermark for Large Language Models","created":"2023-10-10","cited_by_count":112,"area":"safety"},{"id":"2304.01746","title":"Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation","created":"2023-04-04","cited_by_count":97,"area":null},{"id":"2305.13168","title":"LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities","created":"2023-05-22","cited_by_count":97,"area":"lr"},{"id":"2408.09667","title":"BLADE: Benchmarking Language Model Agents for Data-Driven Science","created":"2024-08-19","cited_by_count":77,"area":"agents"},{"id":"2306.07209","title":"Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow","created":"2023-06-12","cited_by_count":76,"area":null},{"id":"2305.14215","title":"Exploring Chain-of-Thought Style Prompting for Text-to-SQL","created":"2023-05-23","cited_by_count":69,"area":"lr"},{"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":"2307.16230","title":"An Unforgeable Publicly Verifiable Watermark for Large Language Models","created":"2023-07-30","cited_by_count":65,"area":"safety"},{"id":"2311.07989","title":"Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code","created":"2023-11-14","cited_by_count":62,"area":null},{"id":"2305.11853","title":"How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings","created":"2023-05-19","cited_by_count":58,"area":null},{"id":"2305.14210","title":"Skill-Based Few-Shot Selection for In-Context Learning","created":"2023-05-23","cited_by_count":46,"area":null},{"id":"2305.12586","title":"Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies","created":"2023-05-21","cited_by_count":45,"area":null},{"id":"2306.00739","title":"SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)","created":"2023-05-26","cited_by_count":42,"area":null},{"id":"2311.09022","title":"Exploring the Potential of Large Language Models in Computational Argumentation","created":"2023-11-15","cited_by_count":42,"area":null},{"id":"2402.11517","title":"Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM","created":"2024-02-18","cited_by_count":35,"area":null},{"id":"2310.13659","title":"Benchmarking and Improving Text-to-SQL Generation under Ambiguity","created":"2023-10-20","cited_by_count":34,"area":null},{"id":"2403.20014","title":"PURPLE: Making a Large Language Model a Better SQL Writer","created":"2024-03-29","cited_by_count":34,"area":null},{"id":"2305.13225","title":"Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance","created":"2023-05-22","cited_by_count":33,"area":null},{"id":"2310.18538","title":"Evaluating Cross-Domain Text-to-SQL Models and Benchmarks","created":"2023-10-27","cited_by_count":32,"area":null},{"id":"2403.02951","title":"SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models","created":"2024-03-05","cited_by_count":32,"area":null},{"id":"2402.10671","title":"Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm","created":"2024-02-16","cited_by_count":31,"area":"lr"},{"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":"2310.17894","title":"Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey","created":"2023-10-27","cited_by_count":27,"area":null},{"id":"2404.09889","title":"Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval","created":"2024-04-15","cited_by_count":27,"area":null},{"id":"2402.14361","title":"OpenTab: Advancing Large Language Models as Open-domain Table Reasoners","created":"2024-02-22","cited_by_count":25,"area":"lr"},{"id":"2306.08891","title":"Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation","created":"2023-06-15","cited_by_count":24,"area":null},{"id":"2311.01173","title":"CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL","created":"2023-11-02","cited_by_count":24,"area":null},{"id":"2402.17882","title":"BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra","created":"2024-02-27","cited_by_count":24,"area":"lr"},{"id":"2312.11988","title":"Xpert: Empowering Incident Management with Query Recommendations via Large Language Models","created":"2023-12-19","cited_by_count":22,"area":null},{"id":"2310.06302","title":"Selective Demonstrations for Cross-domain Text-to-SQL","created":"2023-10-10","cited_by_count":21,"area":null},{"id":"2304.11556","title":"Divide and Prompt: Chain of Thought Prompting for Text-to-SQL","created":"2023-04-23","cited_by_count":20,"area":"lr"},{"id":"2308.10529","title":"SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding","created":"2023-08-21","cited_by_count":18,"area":null},{"id":"2305.03507","title":"Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence","created":"2023-05-02","cited_by_count":17,"area":null},{"id":"2310.15326","title":"Specialist or Generalist? Instruction Tuning for Specific NLP Tasks","created":"2023-10-23","cited_by_count":17,"area":null},{"id":"2402.17144","title":"Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation","created":"2024-02-27","cited_by_count":17,"area":null},{"id":"2502.13550","title":"STaR-SQL: Self-Taught Reasoner for Text-to-SQL","created":"2025-02-19","cited_by_count":17,"area":"lr"},{"id":"2304.13301","title":"Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval","created":"2023-04-26","cited_by_count":16,"area":null},{"id":"2311.02883","title":"SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data","created":"2023-11-06","cited_by_count":16,"area":null},{"id":"2407.03227","title":"Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning","created":"2024-07-03","cited_by_count":16,"area":"rag"},{"id":"2310.13575","title":"Semantic Decomposition of Question and SQL for Text-to-SQL Parsing","created":"2023-10-20","cited_by_count":14,"area":null},{"id":"2412.10138","title":"ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL","created":"2024-12-13","cited_by_count":13,"area":null},{"id":"2307.05074","title":"Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain","created":"2023-07-11","cited_by_count":12,"area":"rag"},{"id":"2401.02115","title":"Using LLM to select the right SQL Query from candidates","created":"2024-01-04","cited_by_count":12,"area":null},{"id":"2405.09593","title":"SQL-to-Schema Enhances Schema Linking in Text-to-SQL","created":"2024-05-15","cited_by_count":12,"area":null},{"id":"2305.01598","title":"From Words to Code: Harnessing Data for Program Synthesis from Natural Language","created":"2023-05-02","cited_by_count":11,"area":null},{"id":"2406.13408","title":"SQLFixAgent: Towards Semantic-Accurate Text-to-SQL Parsing via Consistency-Enhanced Multi-Agent Collaboration","created":"2024-06-19","cited_by_count":11,"area":"agents"},{"id":"2511.10192","title":"Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL","created":"2025-11-13","cited_by_count":11,"area":null},{"id":"2305.16663","title":"GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks","created":"2023-05-26","cited_by_count":10,"area":null},{"id":"2312.10321","title":"LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?","created":"2023-12-16","cited_by_count":10,"area":null},{"id":"2511.21402","title":"Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation","created":"2025-11-26","cited_by_count":10,"area":"lr"},{"id":"2310.18752","title":"Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain","created":"2023-10-28","cited_by_count":9,"area":null},{"id":"2402.11420","title":"Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction","created":"2024-02-18","cited_by_count":9,"area":"lr"},{"id":"2402.13284","title":"Structure Guided Large Language Model for SQL Generation","created":"2024-02-19","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":"2404.14453","title":"EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions","created":"2024-04-21","cited_by_count":8,"area":null},{"id":"2411.02948","title":"Grounding Natural Language to SQL Translation with Data-Based Self-Explanations","created":"2024-11-05","cited_by_count":8,"area":null},{"id":"2504.02327","title":"LearNAT: Learning NL2SQL with AST-guided Task Decomposition for Large Language Models","created":"2025-04-03","cited_by_count":8,"area":null},{"id":"2305.16166","title":"Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis","created":"2023-05-25","cited_by_count":7,"area":null},{"id":"2407.14568","title":"SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy","created":"2024-07-19","cited_by_count":7,"area":null},{"id":"2409.14082","title":"PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL","created":"2024-09-21","cited_by_count":7,"area":"lr"},{"id":"2305.03503","title":"Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction","created":"2023-05-02","cited_by_count":6,"area":null},{"id":"2310.10190","title":"Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison","created":"2023-10-16","cited_by_count":6,"area":null},{"id":"2405.12081","title":"Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model","created":"2024-05-20","cited_by_count":6,"area":null},{"id":"2406.03170","title":"StatBot.Swiss: Bilingual Open Data Exploration in Natural Language","created":"2024-06-05","cited_by_count":6,"area":null},{"id":"2310.16822","title":"Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction","created":"2023-10-25","cited_by_count":5,"area":null},{"id":"2406.00019","title":"EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records","created":"2024-05-23","cited_by_count":5,"area":null},{"id":"2503.04959","title":"DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL","created":"2025-03-06","cited_by_count":5,"area":"synth"},{"id":"2305.16128","title":"Give Me More Details: Improving Fact-Checking with Latent Retrieval","created":"2023-05-25","cited_by_count":4,"area":null},{"id":"2402.08666","title":"Improving Generalization in Semantic Parsing by Increasing Natural Language Variation","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2403.15274","title":"Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review","created":"2024-03-22","cited_by_count":4,"area":null},{"id":"2409.10007","title":"SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL","created":"2024-09-16","cited_by_count":4,"area":"lr"},{"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":"2510.26840","title":"SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification","created":"2025-10-30","cited_by_count":4,"area":null},{"id":"2601.20048","title":"Insight Agents: An LLM-Based Multi-Agent System for Data Insights","created":"2026-01-27","cited_by_count":4,"area":"agents"},{"id":"2312.02251","title":"Fine-Tuning Language Models for Context-Specific SQL Query Generation","created":"2023-12-04","cited_by_count":3,"area":null},{"id":"2312.11282","title":"Evaluating and Enhancing Large Language Models for Conversational Reasoning on Knowledge Graphs","created":"2023-12-18","cited_by_count":3,"area":"lr"},{"id":"2406.00014","title":"KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR","created":"2024-05-22","cited_by_count":3,"area":null},{"id":"2406.16349","title":"AnnotatedTables: A Large Tabular Dataset with Language Model Annotations","created":"2024-06-24","cited_by_count":3,"area":null},{"id":"2412.17964","title":"Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models","created":"2024-12-23","cited_by_count":3,"area":"agents"},{"id":"2601.03475","title":"CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support","created":"2026-01-07","cited_by_count":3,"area":null},{"id":"2306.04480","title":"Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing","created":"2023-05-29","cited_by_count":2,"area":null},{"id":"2405.08839","title":"PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs","created":"2024-05-14","cited_by_count":2,"area":null},{"id":"2409.15907","title":"Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection","created":"2024-09-24","cited_by_count":2,"area":null},{"id":"2412.05561","title":"Can the Rookies Cut the Tough Cookie? Exploring the Use of LLMs for SQL Equivalence Checking","created":"2024-12-07","cited_by_count":2,"area":null},{"id":"2412.05850","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","created":"2024-12-08","cited_by_count":2,"area":"agents"},{"id":"2603.03742","title":"ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement","created":"2026-03-04","cited_by_count":2,"area":null},{"id":"2312.14798","title":"Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databases","created":"2023-12-22","cited_by_count":1,"area":null},{"id":"2410.22082","title":"An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model","created":"2024-10-28","cited_by_count":1,"area":null},{"id":"2411.00788","title":"KeyInst: Keyword Instruction for Improving SQL Formulation in Text-to-SQL","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2412.17942","title":"Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents","created":"2024-12-23","cited_by_count":1,"area":"agents"},{"id":"2511.00985","title":"ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL","created":"2025-11-02","cited_by_count":1,"area":null},{"id":"2512.17053","title":"Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL","created":"2025-12-18","cited_by_count":1,"area":"lr"}],"next_cursor":"100"}