{"total":331,"rows":[{"id":"2305.03111","title":"Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs","created":"2023-05-04","cited_by_count":688,"area":null},{"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":"2211.11501","title":"DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation","created":"2022-11-18","cited_by_count":452,"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":"2201.05966","title":"UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models","created":"2022-01-16","cited_by_count":231,"area":null},{"id":"2210.02875","title":"Binding Language Models in Symbolic Languages","created":"2022-10-06","cited_by_count":228,"area":null},{"id":"2307.09702","title":"Efficient Guided Generation for Large Language Models","created":"2023-07-19","cited_by_count":200,"area":null},{"id":"2305.13971","title":"Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning","created":"2023-05-23","cited_by_count":197,"area":null},{"id":"2302.05965","title":"RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL","created":"2023-02-12","cited_by_count":195,"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":"2201.11227","title":"Synchromesh: Reliable code generation from pre-trained language models","created":"2022-01-26","cited_by_count":156,"area":null},{"id":"2204.11454","title":"Natural Language to Code Translation with Execution","created":"2022-04-25","cited_by_count":124,"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":"2303.13547","title":"A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability","created":"2023-03-12","cited_by_count":119,"area":null},{"id":"2212.06094","title":"Prompting Is Programming: A Query Language for Large Language Models","created":"2022-12-12","cited_by_count":110,"area":null},{"id":"2204.00498","title":"Evaluating the Text-to-SQL Capabilities of Large Language Models","created":"2022-03-15","cited_by_count":101,"area":null},{"id":"2403.06988","title":"Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation","created":"2024-02-07","cited_by_count":101,"area":null},{"id":"2301.07507","title":"Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing","created":"2023-01-18","cited_by_count":93,"area":null},{"id":"2205.06983","title":"RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL","created":"2022-05-14","cited_by_count":85,"area":null},{"id":"2211.08545","title":"MapQA: A Dataset for Question Answering on Choropleth Maps","created":"2022-11-15","cited_by_count":85,"area":null},{"id":"2310.00367","title":"AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ","created":"2023-09-30","cited_by_count":79,"area":null},{"id":"2501.10868","title":"JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models","created":"2025-01-18","cited_by_count":71,"area":null},{"id":"2403.01632","title":"SynCode: LLM Generation with Grammar Augmentation","created":"2024-03-03","cited_by_count":69,"area":null},{"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":"2210.12925","title":"TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases","created":"2022-10-24","cited_by_count":59,"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":"2305.19234","title":"Grammar Prompting for Domain-Specific Language Generation with Large Language Models","created":"2023-05-30","cited_by_count":58,"area":null},{"id":"2212.09736","title":"Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments","created":"2022-12-19","cited_by_count":57,"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":"2209.15565","title":"Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions","created":"2022-09-30","cited_by_count":54,"area":null},{"id":"2405.21047","title":"Grammar-Aligned Decoding","created":"2024-05-31","cited_by_count":54,"area":null},{"id":"2408.03256","title":"Synthesizing Text-to-SQL Data from Weak and Strong LLMs","created":"2024-08-06","cited_by_count":54,"area":null},{"id":"2301.08881","title":"Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness","created":"2023-01-21","cited_by_count":53,"area":null},{"id":"2208.13629","title":"A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions","created":"2022-08-29","cited_by_count":52,"area":null},{"id":"2407.08103","title":"Automata-based constraints for language model decoding","created":"2024-07-11","cited_by_count":51,"area":null},{"id":"2112.08696","title":"Few-Shot Semantic Parsing with Language Models Trained On Code","created":"2021-12-16","cited_by_count":50,"area":null},{"id":"2205.12253","title":"Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing","created":"2022-05-24","cited_by_count":50,"area":null},{"id":"2405.15307","title":"Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation","created":"2024-05-24","cited_by_count":50,"area":null},{"id":"2204.08109","title":"ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering","created":"2022-04-17","cited_by_count":48,"area":null},{"id":"2208.10099","title":"Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect","created":"2022-08-22","cited_by_count":47,"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":"2504.13139","title":"Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo","created":"2025-04-17","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":"2306.04743","title":"ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems","created":"2023-06-07","cited_by_count":45,"area":null},{"id":"2111.00653","title":"SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL","created":"2021-11-01","cited_by_count":43,"area":null},{"id":"2208.08037","title":"LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction","created":"2022-08-17","cited_by_count":43,"area":null},{"id":"2306.14321","title":"RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations","created":"2023-06-25","cited_by_count":43,"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":"2203.06958","title":"S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers","created":"2022-03-14","cited_by_count":41,"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":"2211.07443","title":"Calibrated Interpretation: Confidence Estimation in Semantic Parsing","created":"2022-11-14","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":"2412.03719","title":"From Language Models over Tokens to Language Models over Characters","created":"2024-12-04","cited_by_count":31,"area":null},{"id":"2206.14017","title":"Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing","created":"2022-06-28","cited_by_count":30,"area":null},{"id":"2402.16671","title":"StructLM: Towards Building Generalist Models for Structured Knowledge Grounding","created":"2024-02-26","cited_by_count":29,"area":null},{"id":"2410.07295","title":"IterGen: Iterative Semantic-aware Structured LLM Generation with Backtracking","created":"2024-10-09","cited_by_count":29,"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":"2212.08785","title":"Importance of Synthesizing High-quality Data for Text-to-SQL Parsing","created":"2022-12-17","cited_by_count":28,"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":"2403.09732","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","created":"2024-03-13","cited_by_count":27,"area":null},{"id":"2203.07376","title":"HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing","created":"2022-03-14","cited_by_count":26,"area":null},{"id":"2305.17378","title":"Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques","created":"2023-05-27","cited_by_count":24,"area":null},{"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":"2407.09388","title":"GAVEL: Generating Games Via Evolution and Language Models","created":"2024-07-12","cited_by_count":24,"area":null},{"id":"2209.04994","title":"Knowledge Base Question Answering: A Semantic Parsing Perspective","created":"2022-09-12","cited_by_count":22,"area":null},{"id":"2212.13492","title":"MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing","created":"2022-12-27","cited_by_count":22,"area":null},{"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":"2110.08525","title":"The Power of Prompt Tuning for Low-Resource Semantic Parsing","created":"2021-10-16","cited_by_count":21,"area":null},{"id":"2305.13073","title":"Text-to-SQL Error Correction with Language Models of Code","created":"2023-05-22","cited_by_count":21,"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":"2407.09522","title":"UQE: A Query Engine for Unstructured Databases","created":"2024-06-23","cited_by_count":21,"area":null},{"id":"2504.05410","title":"Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling","created":"2025-04-07","cited_by_count":21,"area":null},{"id":"2203.07781","title":"UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL","created":"2022-03-15","cited_by_count":20,"area":null},{"id":"2306.10763","title":"Guiding Language Models of Code with Global Context using Monitors","created":"2023-06-19","cited_by_count":20,"area":null},{"id":"2501.10858","title":"Reliable Text-to-SQL with Adaptive Abstention","created":"2025-01-18","cited_by_count":20,"area":null},{"id":"2210.10668","title":"N-Best Hypotheses Reranking for Text-To-SQL Systems","created":"2022-10-19","cited_by_count":19,"area":null},{"id":"2210.11888","title":"STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing","created":"2022-10-21","cited_by_count":19,"area":null},{"id":"2305.07372","title":"Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations","created":"2023-05-12","cited_by_count":19,"area":null},{"id":"2301.01067","title":"Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge","created":"2023-01-03","cited_by_count":18,"area":null},{"id":"2211.05165","title":"Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database","created":"2022-11-09","cited_by_count":16,"area":null},{"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":"2112.06311","title":"Weakly Supervised Text-to-SQL Parsing through Question Decomposition","created":"2021-12-12","cited_by_count":15,"area":null},{"id":"2310.14495","title":"InstructExcel: A Benchmark for Natural Language Instruction in Excel","created":"2023-10-23","cited_by_count":15,"area":null},{"id":"2305.16265","title":"UNITE: A Unified Benchmark for Text-to-SQL Evaluation","created":"2023-05-25","cited_by_count":14,"area":null},{"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":"2410.19135","title":"PDL: A Declarative Prompt Programming Language","created":"2024-10-24","cited_by_count":14,"area":null},{"id":"2506.05754","title":"Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective","created":"2025-06-06","cited_by_count":14,"area":null},{"id":"2206.10668","title":"BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing","created":"2022-06-21","cited_by_count":13,"area":null},{"id":"2311.09188","title":"Towards Verifiable Text Generation with Symbolic References","created":"2023-11-15","cited_by_count":13,"area":null},{"id":"2402.12554","title":"Archer: A Human-Labeled Text-to-SQL Dataset with Arithmetic, Commonsense and Hypothetical Reasoning","created":"2024-02-19","cited_by_count":13,"area":null},{"id":"2405.02712","title":"CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions","created":"2024-05-04","cited_by_count":13,"area":null},{"id":"2205.11690","title":"Workflow Discovery from Dialogues in the Low Data Regime","created":"2022-05-24","cited_by_count":12,"area":null},{"id":"2205.12422","title":"Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL","created":"2022-05-25","cited_by_count":12,"area":null},{"id":"2305.13683","title":"Error Detection for Text-to-SQL Semantic Parsing","created":"2023-05-23","cited_by_count":12,"area":null},{"id":"2305.13917","title":"Generating Data for Symbolic Language with Large Language Models","created":"2023-05-23","cited_by_count":12,"area":null}],"next_cursor":"100"}