# Knowledge Base Construction for Knowledge-Augmented Text-to-SQL

arXiv: https://arxiv.org/abs/2505.22096 · HTML: https://mlsanity.com/paper/2505.22096/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://mlsanity.com/paper/2104.06967/) (2021)
- [C3: Zero-shot Text-to-SQL with ChatGPT](https://mlsanity.com/paper/2307.07306/) (2023)
- [MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL](https://mlsanity.com/paper/2312.11242/) (2023)
- [SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning](https://mlsanity.com/paper/1711.04436/) (2017)
- [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://mlsanity.com/paper/2405.04324/) (2024)
- [MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation](https://mlsanity.com/paper/2405.07467/) (2024)
- [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://mlsanity.com/paper/2305.14215/) (2023)
- [How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings](https://mlsanity.com/paper/2305.11853/) (2023)
- [LAB: Large-Scale Alignment for ChatBots](https://mlsanity.com/paper/2403.01081/) (2024)
- [Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM](https://mlsanity.com/paper/2402.11517/) (2024)
- [Prompting as Probing: Using Language Models for Knowledge Base Construction](https://mlsanity.com/paper/2208.11057/) (2022)
- [An Encoder-Decoder Framework Translating Natural Language to Database Queries](https://mlsanity.com/paper/1711.06061/) (2017)
- [Divide and Prompt: Chain of Thought Prompting for Text-to-SQL](https://mlsanity.com/paper/2304.11556/) (2023)
- [Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge](https://mlsanity.com/paper/2301.01067/) (2023)
- [Evaluating the Knowledge Base Completion Potential of GPT](https://mlsanity.com/paper/2310.14771/) (2023)

## Built on by (most cited first, 9 of 9)

- [Arming Data Agents with Tribal Knowledge](https://mlsanity.com/paper/2602.13521/) (2026)
- [RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System](https://mlsanity.com/paper/2508.17590/) (2025)
- [Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel](https://mlsanity.com/paper/2603.12483/) (2026)
- [GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models](https://mlsanity.com/paper/2509.01308/) (2025)
- [Automatic Prompt Engineering with No Task Cues and No Tuning](https://mlsanity.com/paper/2601.03130/) (2026)
- [Bootstrapping Semantic Layer from Execution for Text-to-SQL](https://mlsanity.com/paper/2606.05634/) (2026)
- [SDAM: Structure-Difference-Aware Memory Evolution for Complex Text-to-SQL](https://mlsanity.com/paper/2608.12338/) (2026)
- [Beyond the Harness: End-to-End Optimization of Context Artifacts for Enterprise Text-to-SQL](https://mlsanity.com/paper/2608.22830/) (2026)
- [Beyond Context Windows: Persistent Discovery Context for Data-Centric Agents](https://mlsanity.com/paper/2609.02129/) (2026)

## Abstract (arXiv)

Text-to-SQL aims to translate natural language queries into SQL statements, which is practical as it enables anyone to easily retrieve the desired information from databases. Recently, many existing approaches tackle this problem with Large Language Models (LLMs), leveraging their strong capability in understanding user queries and generating corresponding SQL code. Yet, the parametric knowledge in LLMs might be limited to covering all the diverse and domain-specific queries that require grounding in various database schemas, which makes generated SQLs less accurate oftentimes. To tackle this, we propose constructing the knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. In particular, unlike existing approaches that either manually annotate knowledge or generate only a few pieces of knowledge for each query, our knowledge base is comprehensive, which is constructed based on a combination of all the available questions and their associated database schemas along with their relevant knowledge, and can be reused for unseen databases from different datasets and domains. We validate our approach on multiple text-to-SQL datasets, considering both the overlapping and non-overlapping database scenarios, where it outperforms relevant baselines substantially.