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Reimagining Text-to-SQL: A Survey of Large Language Model Architectures and Strategies

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 03/11/2026, 09:03:24 UTC.

Briefings

This survey examines over 100 papers and technical resources on Text-to-SQL systems, analyzing LLM-driven approaches from 2022 to 2026. Key findings include the emergence of five paradigms (in-context learning, fine-tuning, agentic orchestration, RAG, and RL) and a characterization of models by granularity, supervision level, and reasoning style. The survey identifies execution accuracy improvements on benchmarks like Spider and Spider 2.0, alongside challenges in domain generalization, efficiency, and real-world deployment. It also discusses responsible AI practices as LLMs access sensitive databases.

Key points

• The survey covers over 100 papers and technical resources on Text-to-SQL systems published between 2022 and 2026.
• Five paradigms are identified: in-context learning, supervised/fine-tuning, agentic orchestration, RAG, and reinforcement learning.

Context

Text-to-SQL systems translate natural language questions into executable SQL queries, democratizing access to relational data. This survey reviews over 100 papers and technical resources, focusing on work published between 2022 and 2026.

Limitations

Evidence is limited to the provided abstract and summary of the arXiv paper. Specific author names, exact dataset performance metrics beyond general benchmark mentions, and detailed deployment case studies are not included in the source evidence.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 03/11/2026, 09:07:01 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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