{"list":"other","total":57,"rows":[{"id":"1809.08887","title":"Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task","created":"2018-09-24","cited_by_count":1139},{"id":"1709.00103","title":"Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning","created":"2017-08-31","cited_by_count":687},{"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},{"id":"1911.04942","title":"RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers","created":"2019-11-10","cited_by_count":424},{"id":"2109.05093","title":"PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models","created":"2021-09-10","cited_by_count":411},{"id":"2212.10450","title":"Is GPT-3 a Good Data Annotator?","created":"2022-12-20","cited_by_count":208},{"id":"2302.05965","title":"RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL","created":"2023-02-12","cited_by_count":195},{"id":"2312.11242","title":"MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL","created":"2023-12-18","cited_by_count":164},{"id":"1711.04436","title":"SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning","created":"2017-11-13","cited_by_count":135},{"id":"2406.08426","title":"Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL","created":"2024-06-12","cited_by_count":120},{"id":"2104.07788","title":"PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models","created":"2021-04-15","cited_by_count":118},{"id":"2402.01801","title":"Large Language Models for Time Series: A Survey","created":"2024-02-02","cited_by_count":114},{"id":"2310.10196","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","created":"2023-10-16","cited_by_count":110},{"id":"2106.11455","title":"KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers","created":"2021-06-22","cited_by_count":106},{"id":"1902.01069","title":"A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization","created":"2019-02-04","cited_by_count":88},{"id":"1804.09769","title":"TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation","created":"2018-04-25","cited_by_count":81},{"id":"1810.05237","title":"SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task","created":"2018-10-11","cited_by_count":74},{"id":"2407.15186","title":"A Survey on Employing Large Language Models for Text-to-SQL Tasks","created":"2024-07-21","cited_by_count":67},{"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},{"id":"2305.10716","title":"A Survey on Time-Series Pre-Trained Models","created":"2023-05-18","cited_by_count":52},{"id":"2106.05006","title":"Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data","created":"2021-06-09","cited_by_count":43},{"id":"2306.00739","title":"SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)","created":"2023-05-26","cited_by_count":42},{"id":"1906.08990","title":"A Comparative Survey of Recent Natural Language Interfaces for Databases","created":"2019-06-21","cited_by_count":33},{"id":"2310.17894","title":"Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey","created":"2023-10-27","cited_by_count":27},{"id":"2410.06011","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","created":"2024-10-08","cited_by_count":24},{"id":"2503.19988","title":"ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback","created":"2025-03-25","cited_by_count":24},{"id":"1905.13326","title":"Grammar-based Neural Text-to-SQL Generation","created":"2019-05-30","cited_by_count":23},{"id":"2504.06969","title":"Towards LLMs Robustness to Changes in Prompt Format Styles","created":"2025-04-09","cited_by_count":21},{"id":"2502.11741","title":"SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL","created":"2025-02-17","cited_by_count":19},{"id":"2010.12634","title":"Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL","created":"2020-10-23","cited_by_count":16},{"id":"2509.23988","title":"LLM/Agent-as-Data-Analyst: A Survey","created":"2025-09-28","cited_by_count":16},{"id":"2408.14806","title":"Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications","created":"2024-08-27","cited_by_count":13},{"id":"2502.18470","title":"Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions","created":"2025-02-04","cited_by_count":13},{"id":"2110.01839","title":"Truth-Conditional Captioning of Time Series Data","created":"2021-10-05","cited_by_count":11},{"id":"2407.14767","title":"I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation","created":"2024-07-20","cited_by_count":11},{"id":"2412.05208","title":"A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges","created":"2024-12-06","cited_by_count":10},{"id":"2308.16060","title":"Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap","created":"2023-08-30","cited_by_count":9},{"id":"2412.10434","title":"NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language","created":"2024-12-11","cited_by_count":9},{"id":"2509.24405","title":"Multilingual Text-to-SQL: Benchmarking the Limits of Language Models with Collaborative Language Agents","created":"2025-09-29","cited_by_count":8},{"id":"2311.01862","title":"$R^3$-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL","created":"2023-11-03","cited_by_count":7},{"id":"2508.19305","title":"Geo2Vec: Shape- and Distance-Aware Neural Representation of Geospatial Entities","created":"2025-08-26","cited_by_count":7},{"id":"2409.16647","title":"Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data","created":"2024-09-25","cited_by_count":6},{"id":"2411.08397","title":"CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision","created":"2024-11-13","cited_by_count":6},{"id":"1908.10917","title":"SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension","created":"2019-08-28","cited_by_count":5},{"id":"2503.02435","title":"NLI4DB: A Systematic Review of Natural Language Interfaces for Databases","created":"2025-03-04","cited_by_count":5},{"id":"2504.14837","title":"SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation","created":"2025-04-21","cited_by_count":5},{"id":"2511.06778","title":"SAFENLIDB: A Privacy-Preserving Safety Alignment Framework for LLM-based Natural Language Database Interfaces","created":"2025-11-10","cited_by_count":4},{"id":"2503.03114","title":"PromCopilot: Simplifying Prometheus Metric Querying in Cloud Native Online Service Systems via Large Language Models","created":"2025-03-05","cited_by_count":3},{"id":"2503.14251","title":"Towards a Barrier-free GeoQA Portal: Natural Language Interaction with Geospatial Data Using Multi-Agent LLMs and Semantic Search","created":"2025-03-18","cited_by_count":3},{"id":"2406.07913","title":"DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning","created":"2024-06-12","cited_by_count":2},{"id":"2503.21378","title":"Retrieving Time-Series Differences Using Natural Language Queries","created":"2025-03-27","cited_by_count":2},{"id":"2504.17833","title":"The Role of Open-Source LLMs in Shaping the Future of GeoAI","created":"2025-04-24","cited_by_count":2},{"id":"2509.25264","title":"GeoSQL-Eval: First Evaluation of LLMs on PostGIS-Based NL2GeoSQL Queries","created":"2025-09-28","cited_by_count":2},{"id":"2510.10661","title":"AGENTIQL: An Agent-Inspired Multi-Expert Framework for Text-to-SQL Generation","created":"2025-10-12","cited_by_count":2},{"id":"2510.21045","title":"From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL","created":"2025-10-23","cited_by_count":2},{"id":"2510.25997","title":"From Queries to Insights: Agentic LLM Pipelines for Spatio-Temporal Text-to-SQL","created":"2025-10-29","cited_by_count":2},{"id":"2510.27532","title":"SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps","created":"2025-10-31","cited_by_count":1}],"next_cursor":null}