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Text-To-SQL

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보존된 평가 기록: 86

Text-to-SQL is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.

<span style="color:grey; opacity: 0.6">( Image credit: SyntaxSQLNet )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/2003pro/exesql

공식 코드

ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects

https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/proton

공식 코드

Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

https://github.com/ElementAI/duorat

공식 코드

DuoRAT: Towards Simpler Text-to-SQL Models

https://github.com/ElementAI/picard

공식 코드

PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

https://github.com/GenseeAI/cognify

공식 코드

Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning

https://github.com/HasanAlpCaferoglu/E-SQL

공식 코드

E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL

https://github.com/JuruoMP/T5-SR

공식 코드

T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing

https://github.com/LancelotXWX/MAG-SQL

공식 코드

MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQL

https://github.com/Lumen-Laboratory/OpenGrok

공식 코드

OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms

https://github.com/MJinXiang/Reasoning-Table

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Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning

https://github.com/ML-KULeuven/deepstochlog-lm

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Valid Text-to-SQL Generation with Unification-based DeepStochLog

https://github.com/Microsoft/rat-sql

공식 코드

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

https://github.com/Nickolas-option/QA_on_Tabular_Data_SemEval2025_Task8

공식 코드

Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA

https://github.com/PaddlePaddle/Research

공식 코드

Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing

https://github.com/RUCKBReasoning/DPO_Text2SQL

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Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL

https://github.com/RUCKBReasoning/OmniSQL

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OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale

https://github.com/Rcrossmeister/Knowledge-to-SQL

공식 코드

Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM

https://github.com/SamuelGabriel/sqlbpe

공식 코드

Byte-Pair Encoding for Text-to-SQL Generation

https://github.com/Satissss/LinkAlign

공식 코드

LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL

https://github.com/StanleySun233/maritime-vts

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VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language

https://github.com/VinAIResearch/ViText2SQL

공식 코드

A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese

https://github.com/XGenerationLab/XiYan-SQL

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A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

https://github.com/ZhaoFuheng/LLM-SQL-Solver

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LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?

https://github.com/adrianbzg/sqlformer

공식 코드

SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation

https://github.com/ahmedheakl/araspider

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AraSpider: Democratizing Arabic-to-SQL

https://github.com/alibaba/route

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ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL

https://github.com/alibabaresearch/damo-convai

공식 코드

Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

https://github.com/amolk/spider-schema-gnn/blob/discriminator/GNNDiscrimNet.ipynb

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Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker

https://github.com/appier-research/i-need-help

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I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation

https://github.com/awslabs/unified-text2sql-benchmark

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UNITE: A Unified Benchmark for Text-to-SQL Evaluation

https://github.com/bahuia/sscl-text2sql

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Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

https://github.com/bayer-group/text-to-sql-epi-ehr-naacl2024

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Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records

https://github.com/beachwang/dail-sql

공식 코드

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

https://github.com/benbogin/spider-schema-gnn

공식 코드

Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

https://github.com/benbogin/spider-schema-gnn-global

공식 코드

Global Reasoning over Database Structures for Text-to-SQL Parsing

https://github.com/berlino/tensor2struct-public

공식 코드

Learning to Synthesize Data for Semantic Parsing

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