Summary of End-to-end Text-to-sql Generation Within An Analytics Insight Engine, by Karime Maamari et al.
End-to-end Text-to-SQL Generation within an Analytics Insight Engine
by Karime Maamari, Amine Mhedhbi
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (cs.LG)
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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 The paper discusses the advancements in Text-to-SQL technology, which enables greater democratization of data access by empowering language models to generate SQL queries. The authors highlight the importance of considering three core challenges: supporting high-complexity SQL query authoring, providing low-latency requests, and incorporating domain-specific terminology and practices. Specifically, they cite Distyl AI’s Analytics Insight Engine as an example of impressive Text-to-SQL generation, which has been deployed with enterprise customers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how computers can understand and generate SQL code from natural language text. The authors want to make it easier for people without technical skills to access data. They use a special AI model called the Analytics Insight Engine to show that this is possible. The problem is that some people need very complex queries, while others need quick results. The engine needs to understand specific words and ideas related to different areas of expertise. |
