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Text-To-SQL

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하위 태스크: 1
평가표: 10
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/juruomp/rat-sql-tc

공식 코드

Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL

https://github.com/kakaoenterprise/RYANSQL

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RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases

https://github.com/karthikradhakrishnan96/ColloQL

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ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queries

https://github.com/laqcce-cao/rsl-sql

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RSL-SQL: Robust Schema Linking in Text-to-SQL Generation

https://github.com/layer6ai-labs/msc-sql

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MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation

https://github.com/leuchine/self_play_picard

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Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play

https://github.com/lrlbbzl/ptd-sql

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PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL

https://github.com/lumia-group/rasat

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RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL

https://github.com/lyuqin/HydraNet-WikiSQL

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Hybrid Ranking Network for Text-to-SQL

https://github.com/magic-yuantian/steps

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Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations

https://github.com/mahadi-nahid/tabsqlify

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TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition

https://github.com/mcgill-nlp/magnifico

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MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations

https://github.com/mcxiaoxiao/MMSQL

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Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types

https://github.com/mcxiaoxiao/qda-sql

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QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL

https://github.com/mdfahimanjum/llm-planning-with-reasoning

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When Reasoning Beats Scale: A 1.5B Reasoning Model Outranks 13B LLMs as Discriminator

https://github.com/mercatorhq/dubo-sql

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Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL

https://github.com/microsoft/ContextualSP

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"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL

https://github.com/microsoft/synqo

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MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL

https://github.com/mohammadrezapourreza/few-shot-nl2sql-with-prompting

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DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

https://github.com/neuripspublishingresearchcode/select-sql

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SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL

https://github.com/niansong1996/lever

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LEVER: Learning to Verify Language-to-Code Generation with Execution

https://github.com/nih-card/biomedsql

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BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases

https://github.com/niklaswretblad/the-effects-of-noise-in-text-to-SQL

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Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark

https://github.com/nl2sql-empirical/nl2sql_error_empirical

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A Study of In-Context-Learning-Based Text-to-SQL Errors

https://github.com/nzjin/odtqa

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Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval

https://github.com/osu-nlp-group/auto-sql-correction

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Text-to-SQL Error Correction with Language Models of Code

https://github.com/osu-nlp-group/fl4semanticparsing

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Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms

https://github.com/osu-nlp-group/llm-planning-eval

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When is Tree Search Useful for LLM Planning? It Depends on the Discriminator

https://github.com/osu-nlp-group/text2sql-error-detection

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Error Detection for Text-to-SQL Semantic Parsing

https://github.com/parkervg/destt5

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Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding

https://github.com/qjay612/meta_learning_NL2SQL

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Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning

https://github.com/quge2023/TA-SQL

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Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation

https://github.com/rhythmcao/text2sql-lgesql

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LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations

https://github.com/ruc-datalab/rewardsql

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Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards

https://github.com/ruckbreasoning/codes

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CodeS: Towards Building Open-source Language Models for Text-to-SQL

https://github.com/ruckbreasoning/resdsql

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RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.