Summary of A Survey Of Large Language Model-based Generative Ai For Text-to-sql: Benchmarks, Applications, Use Cases, and Challenges, by Aditi Singh et al.
A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges
by Aditi Singh, Akash Shetty, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides a comprehensive overview of AI-driven text-to-SQL systems, which translate natural language queries into Structured Query Language (SQL). It highlights their foundational components, advancements in large language model (LLM) architectures, and the critical role of datasets such as Spider, WikiSQL, and CoSQL. The applications of text-to-SQL are examined in domains like healthcare, education, and finance, emphasizing their transformative potential for improving data accessibility. Persistent challenges include domain generalization, query optimization, support for multi-turn conversational interactions, and limited availability of tailored datasets for NoSQL databases and dynamic real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text-to-SQL systems help people talk to databases without needing to know how to code. This paper shows what these systems do well and where they need improvement. It talks about the big datasets that make them work better and how they can be used in different fields like healthcare and education. The paper also mentions some problems with these systems, like making sure they work across different areas of expertise and supporting conversations that go back and forth. |
Keywords
» Artificial intelligence » Domain generalization » Large language model » Optimization
