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

Summary: Systematic study of extended LLM context windows for NL2SQL with gemini‑1.5‑pro, measuring how context types (column values, Q–SQL examples, hints, docs, schema) affect accuracy vs latency. Finds long‑context LLMs robust and deliver strong zero‑shot NL2SQL without fine‑tuning. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13915
Venue
VLDB
Year
2025
Pagerank
4.3690661e-05
Overall Rank
9,234 | 35.77%
DOI
10.14778/3742728.3742761

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,194 PRISM: Navigating Cost–Accuracy Trade-offs for NL2SQL 2026 SIGMOD 4.1945683e-05
10,837 Natural Language to SQL: State of the Art and Open Problems 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
369 Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation 2024 VLDB 0.0002547515
2,988 NL2SQL is a solved problem... Not! 2024 CIDR 7.7761714e-05
3,662 The Dawn of Natural Language to SQL: Are We Fully Ready? 2024 VLDB 6.8672143e-05
6,826 Natural Language Interfaces for Databases with Deep Learning 2023 VLDB 4.9142824e-05
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