# Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL

arXiv: https://arxiv.org/abs/2501.12372 · HTML: https://mlsanity.com/paper/2501.12372/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 58 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 29 of 29)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [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)
- [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)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (2024)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [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)
- [Large Language Models Meet NL2Code: A Survey](https://mlsanity.com/paper/2212.09420/) (2022)
- [CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL](https://mlsanity.com/paper/2410.01943/) (2024)
- [KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers](https://mlsanity.com/paper/2106.11455/) (2021)
- [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)
- [The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models](https://mlsanity.com/paper/2408.07702/) (2024)
- [Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies](https://mlsanity.com/paper/2305.12586/) (2023)
- [BEAVER: An Enterprise Benchmark for Text-to-SQL](https://mlsanity.com/paper/2409.02038/) (2024)
- [E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL](https://mlsanity.com/paper/2409.16751/) (2024)
- [A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases](https://mlsanity.com/paper/2311.07509/) (2023)
- [Divide and Prompt: Chain of Thought Prompting for Text-to-SQL](https://mlsanity.com/paper/2304.11556/) (2023)
- [Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!](https://mlsanity.com/paper/2405.11706/) (2024)
- [Blar-SQL: Faster, Stronger, Smaller NL2SQL](https://mlsanity.com/paper/2401.02997/) (2024)

## Built on by (most cited first, 30 of 30)

- [Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models](https://mlsanity.com/paper/2510.04618/) (2025)
- [How Many Instructions Can LLMs Follow at Once?](https://mlsanity.com/paper/2507.11538/) (2025)
- [Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL](https://mlsanity.com/paper/2504.15077/) (2025)
- [ContextPilot: Fast Long-Context Inference via Context Reuse](https://mlsanity.com/paper/2511.03475/) (2025)
- [Arming Data Agents with Tribal Knowledge](https://mlsanity.com/paper/2602.13521/) (2026)
- [Enhancing Text2Cypher with Schema Filtering](https://mlsanity.com/paper/2505.05118/) (2025)
- [Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL](https://mlsanity.com/paper/2510.14296/) (2025)
- [Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents](https://mlsanity.com/paper/2602.01405/) (2026)
- [RetrySQL: text-to-SQL training with retry data for self-correcting query generation](https://mlsanity.com/paper/2507.02529/) (2025)
- [LLM-Powered Proactive Data Systems](https://mlsanity.com/paper/2502.13016/) (2025)
- [High-Fidelity And Complex Test Data Generation For Google SQL Code Generation Services](https://mlsanity.com/paper/2504.17203/) (2025)
- [SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation](https://mlsanity.com/paper/2509.19623/) (2025)
- [On Theoretically-Driven LLM Agents for Multi-Dimensional Discourse Analysis](https://mlsanity.com/paper/2602.13713/) (2026)
- [Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?](https://mlsanity.com/paper/2602.21480/) (2026)
- [Fine-Grained Table Retrieval Through the Lens of Complex Queries](https://mlsanity.com/paper/2603.07146/) (2026)
- [AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views](https://mlsanity.com/paper/2604.07041/) (2026)
- [Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering](https://mlsanity.com/paper/2605.10318/) (2026)
- [Effectiveness of Prompt Optimization in NL2SQL Systems](https://mlsanity.com/paper/2505.20591/) (2025)
- [Context-Driven Performance Modeling for Causal Inference Operators on Neural Processing Units](https://mlsanity.com/paper/2509.25155/) (2025)
- [FINER-SQL: Boosting Small Language Models for Text-to-SQL](https://mlsanity.com/paper/2605.03465/) (2026)
- [Toward Multi-Database Query Reasoning for Text2Cypher](https://mlsanity.com/paper/2605.10373/) (2026)
- [VineLM: Trie-Based Fine-Grained Control for Agentic Workflows](https://mlsanity.com/paper/2605.23914/) (2026)
- [Database Context Compression for Text-to-SQL on Real-World Large Databases](https://mlsanity.com/paper/2606.28601/) (2026)
- [STRATOS: Bridging the Symbolic-to-Numeric Gap in Spatio-Temporal Text-to-SQL for Meteorological Data](https://mlsanity.com/paper/2607.03501/) (2026)
- [Benchmarking Text-to-SQL under Role-Based Access Control](https://mlsanity.com/paper/2607.22115/) (2026)
- [DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation](https://mlsanity.com/paper/2608.11889/) (2026)
- [SchemaLink: An Intelligent Web Editor for LinkML Schema Curation](https://mlsanity.com/paper/2608.12529/) (2026)
- [DualSQL: Text-to-SQL with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2609.18135/) (2026)
- [BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL](https://mlsanity.com/paper/2610.00092/) (2026)
- [Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows](https://mlsanity.com/paper/2610.02122/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. In particular, improvements in reasoning abilities and the expansion of context windows have opened new avenues for leveraging these powerful models. NL2SQL is challenging in that the natural language question is inherently ambiguous, while the SQL generation requires a precise understanding of complex data schema and semantics. One approach to this semantic ambiguous problem is to provide more and sufficient contextual information. In this work, we explore the performance and the latency trade-offs of the extended context window (a.k.a., long context) offered by Google's state-of-the-art LLM (textitgemini-1.5-pro). We study the impact of various contextual information, including column example values, question and SQL query pairs, user-provided hints, SQL documentation, and schema. To the best of our knowledge, this is the first work to study how the extended context window and extra contextual information can help NL2SQL generation with respect to both accuracy and latency cost. We show that long context LLMs are robust and do not get lost in the extended contextual information. Additionally, our long-context NL2SQL pipeline based on Google's textitgemini-pro-1.5 achieve strong performances on various benchmark datasets without finetuning and expensive self-consistency based techniques.