A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL
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Feb 19, 2025 - Python
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A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL
This is a continuously updated handbook for readers to easily track the latest NL2SQL (Text-to-SQL) techniques in the literature and provide practical guidance for researchers and practitioners. If we missed any interesting work, feel free to contact us.
Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
A solution guidance for Generative BI using Amazon Bedrock, Amazon OpenSearch with RAG
Official repository for the paper “The Dawn of Natural Language to SQL: Are We Fully Ready?” (VLDB'24)
Chat2Graph: Graph Native Agentic System.
RSL-SQL: Robust Schema Linking in Text-to-SQL Generation
GAP-text2SQL: Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Convert natural language query to appropriate SQL, make ERPs cool again.
🌶️ R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)
Data Neuron is a powerful framework that enables you to build text-to-SQL applications with an easily maintainable semantic layer. Whether you're creating customer-facing chatbots, internal Slack bots for analytics, or other data-driven applications, Data Neuron provides the tools to make your data accessible through natural language
Fine-Tuning Dataset Auto-Generation for Graph Query Languages.
The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"
EvalBench is a flexible framework designed to measure the quality of generative AI (GenAI) workflows around database specific tasks.
Table2answer: Read the database and answer without SQL https://arxiv.org/abs/1902.04260
Using Database Rule for Weak Supervised Text-to-SQL Generation https://arxiv.org/abs/1907.00620
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