"A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges"

This comprehensive survey examines how LLMs transform Text-to-SQL systems, analyzing benchmarks, applications, and challenges. It provides insights into system architectures, evaluation frameworks, and real-world implementations across healthcare, education, and finance sectors.

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https://arxiv.org/abs/2412.05208

🤔 Original Problem:

Non-technical users struggle to interact with databases effectively, creating a gap between natural language queries and SQL execution. Traditional approaches lack robustness in handling complex queries and cross-domain applications.

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🔧 Solution in this Paper:

→ The paper presents a systematic analysis of Text-to-SQL systems focusing on LLM architectures and foundational components.

→ It examines key benchmarks like Spider, WikiSQL, and CoSQL for evaluating system performance.

→ The solution incorporates schema linking, natural language understanding, and semantic parsing to bridge the query-execution gap.

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💡 Key Insights:

→ Text-to-SQL systems require domain-specific optimizations beyond general-purpose AI models

→ Schema linking plays a crucial role in accurately mapping user intents to database structures

→ Multi-turn conversational interactions remain a significant challenge

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📊 Results:

→ Spider 2.0 framework evaluates 632 real-world workflow problems with 1,000+ columns

→ BIRD dataset covers 12,751 question-SQL pairs across 37 professional domains

→ CoSQL achieves coverage of 30,000 turns and 10,000 annotated SQL queries

First Set:

Text-to-SQL systems make databases speak human language through LLM-powered translation

LLMs bridge the gap between natural conversations and database queries

Natural language to SQL conversion simplified through intelligent LLM architectures

Database interaction becomes human-friendly with LLM-powered query translation

Second Set:

Want to chat with your database? This paper shows how LLMs make it possible

Databases finally understand humans, thanks to LLM magic

Talk to your database like a friend - LLMs make it happen

No more SQL headaches - just tell your database what you want