{"list":"other","total":94,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2201.11903","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","created":"2022-01-28","cited_by_count":12459},{"id":"2107.03374","title":"Evaluating Large Language Models Trained on Code","created":"2021-07-07","cited_by_count":8320},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"2206.07682","title":"Emergent Abilities of Large Language Models","created":"2022-06-15","cited_by_count":2512},{"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":"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},{"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":"2005.08314","title":"TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data","created":"2020-05-17","cited_by_count":398},{"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},{"id":"1905.08205","title":"Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation","created":"2019-05-20","cited_by_count":210},{"id":"2302.05965","title":"RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL","created":"2023-02-12","cited_by_count":195},{"id":"1909.05378","title":"CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases","created":"2019-09-11","cited_by_count":185},{"id":"2009.13845","title":"GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing","created":"2020-09-29","cited_by_count":171},{"id":"2307.07306","title":"C3: Zero-shot Text-to-SQL with ChatGPT","created":"2023-07-14","cited_by_count":164},{"id":"1601.01280","title":"Language to Logical Form with Neural Attention","created":"2016-01-06","cited_by_count":158},{"id":"2010.12725","title":"Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?","created":"2020-10-24","cited_by_count":147},{"id":"1906.02285","title":"SParC: Cross-Domain Semantic Parsing in Context","created":"2019-06-05","cited_by_count":144},{"id":"1711.04436","title":"SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning","created":"2017-11-13","cited_by_count":135},{"id":"1806.09029","title":"Improving Text-to-SQL Evaluation Methodology","created":"2018-06-23","cited_by_count":135},{"id":"2012.12627","title":"Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing","created":"2020-12-23","cited_by_count":133},{"id":"2010.02840","title":"Semantic Evaluation for Text-to-SQL with Distilled Test Suites","created":"2020-10-06","cited_by_count":126},{"id":"2010.12773","title":"Structure-Grounded Pretraining for Text-to-SQL","created":"2020-10-24","cited_by_count":126},{"id":"2106.01093","title":"LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations","created":"2021-06-02","cited_by_count":119},{"id":"2303.13547","title":"A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability","created":"2023-03-12","cited_by_count":119},{"id":"1805.04793","title":"Coarse-to-Fine Decoding for Neural Semantic Parsing","created":"2018-05-12","cited_by_count":110},{"id":"2106.01065","title":"Towards Robustness of Text-to-SQL Models against Synonym Substitution","created":"2021-06-02","cited_by_count":110},{"id":"2204.00498","title":"Evaluating the Text-to-SQL Capabilities of Large Language Models","created":"2022-03-15","cited_by_count":101},{"id":"1704.08760","title":"Learning a Neural Semantic Parser from User Feedback","created":"2017-04-27","cited_by_count":99},{"id":"1802.08979","title":"NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System","created":"2018-02-25","cited_by_count":98},{"id":"2010.12412","title":"SmBoP: Semi-autoregressive Bottom-up Semantic Parsing","created":"2020-10-23","cited_by_count":97},{"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},{"id":"2109.05157","title":"Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization","created":"2021-09-11","cited_by_count":92},{"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":"1909.00786","title":"Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions","created":"2019-09-02","cited_by_count":80},{"id":"1908.01839","title":"Text-to-SQL Generation for Question Answering on Electronic Medical Records","created":"2019-07-28","cited_by_count":77},{"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":"1905.06241","title":"Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing","created":"2019-05-15","cited_by_count":74},{"id":"2008.10723","title":"NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries","created":"2020-08-24","cited_by_count":74},{"id":"2305.14215","title":"Exploring Chain-of-Thought Style Prompting for Text-to-SQL","created":"2023-05-23","cited_by_count":69},{"id":"2109.05153","title":"Natural SQL: Making SQL Easier to Infer from Natural Language Specifications","created":"2021-09-11","cited_by_count":68},{"id":"2012.10309","title":"Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training","created":"2020-12-18","cited_by_count":65},{"id":"2004.03125","title":"RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases","created":"2020-04-07","cited_by_count":63},{"id":"1812.00978","title":"A Survey on Semantic Parsing","created":"2018-12-03","cited_by_count":62},{"id":"2109.03506","title":"Towards Natural Language Interfaces for Data Visualization: A Survey","created":"2021-09-08","cited_by_count":59},{"id":"2302.02094","title":"Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models","created":"2023-02-04","cited_by_count":59},{"id":"2301.08881","title":"Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness","created":"2023-01-21","cited_by_count":53},{"id":"2204.07356","title":"Vision-and-Language Pretrained Models: A Survey","created":"2022-04-15","cited_by_count":50},{"id":"1804.03126","title":"Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks","created":"2018-04-09","cited_by_count":48},{"id":"2209.14500","title":"Bidirectional Language Models Are Also Few-shot Learners","created":"2022-09-29","cited_by_count":48},{"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},{"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},{"id":"1904.04446","title":"HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition","created":"2019-04-09","cited_by_count":43},{"id":"1908.11214","title":"Global Reasoning over Database Structures for Text-to-SQL Parsing","created":"2019-08-29","cited_by_count":43},{"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":"2111.00653","title":"SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL","created":"2021-11-01","cited_by_count":43},{"id":"2010.01891","title":"A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese","created":"2020-10-05","cited_by_count":36},{"id":"2104.04689","title":"ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser","created":"2021-04-10","cited_by_count":35},{"id":"1906.08990","title":"A Comparative Survey of Recent Natural Language Interfaces for Databases","created":"2019-06-21","cited_by_count":33},{"id":"1909.13293","title":"A Pilot Study for Chinese SQL Semantic Parsing","created":"2019-09-29","cited_by_count":33},{"id":"2008.04759","title":"Hybrid Ranking Network for Text-to-SQL","created":"2020-08-11","cited_by_count":33},{"id":"2110.00680","title":"Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations","created":"2021-10-01","cited_by_count":31},{"id":"2112.12926","title":"nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task","created":"2021-12-24","cited_by_count":28},{"id":"2007.15280","title":"Photon: A Robust Cross-Domain Text-to-SQL System","created":"2020-07-30","cited_by_count":26},{"id":"1908.00681","title":"FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System","created":"2019-08-02","cited_by_count":25},{"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},{"id":"1908.08113","title":"X-SQL: reinforce schema representation with context","created":"2019-08-21","cited_by_count":22},{"id":"2203.07781","title":"UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL","created":"2022-03-15","cited_by_count":20},{"id":"2304.11556","title":"Divide and Prompt: Chain of Thought Prompting for Text-to-SQL","created":"2023-04-23","cited_by_count":20},{"id":"2301.01067","title":"Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge","created":"2023-01-03","cited_by_count":18},{"id":"2205.02054","title":"Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment","created":"2022-05-04","cited_by_count":16},{"id":"1902.00031","title":"Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases","created":"2019-01-31","cited_by_count":15},{"id":"2110.03546","title":"mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer","created":"2021-10-07","cited_by_count":15},{"id":"2103.08886","title":"Automatic Intent-Slot Induction for Dialogue Systems","created":"2021-03-16","cited_by_count":13},{"id":"2001.06423","title":"InChorus: Designing Consistent Multimodal Interactions for Data Visualization on Tablet Devices","created":"2020-01-17","cited_by_count":12},{"id":"2006.06434","title":"TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation","created":"2020-06-10","cited_by_count":12},{"id":"2207.00189","title":"Facilitating Conversational Interaction in Natural Language Interfaces for Visualization","created":"2022-07-01","cited_by_count":12},{"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},{"id":"2307.16013","title":"Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations","created":"2023-07-29","cited_by_count":9},{"id":"2209.08834","title":"NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries","created":"2022-09-19","cited_by_count":8},{"id":"2106.14614","title":"Progressive Open-Domain Response Generation with Multiple Controllable Attributes","created":"2021-06-07","cited_by_count":6},{"id":"1809.05054","title":"IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles","created":"2018-09-13","cited_by_count":5},{"id":"2103.08893","title":"KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph","created":"2021-03-16","cited_by_count":4},{"id":"2309.07650","title":"Automatic Data Visualization Generation from Chinese Natural Language Questions","created":"2023-09-14","cited_by_count":4},{"id":"2010.04948","title":"Making Online Sketching Hashing Even Faster","created":"2020-10-10","cited_by_count":3},{"id":"2010.04966","title":"Effective Data-aware Covariance Estimator from Compressed Data","created":"2020-10-10","cited_by_count":2}],"next_cursor":null}