Summary of Next-generation Database Interfaces: a Survey Of Llm-based Text-to-sql, by Zijin Hong et al.
Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL
by Zijin Hong, Zheng Yuan, Qinggang Zhang, Hao Chen, Junnan Dong, Feiran Huang, Xiao Huang
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents an overview of large language model (LLM) based solutions for generating accurate SQL from users’ natural language questions (text-to-SQL). The text-to-SQL task involves understanding user questions, comprehending database schema, and generating correct SQL. Traditional approaches combining human engineering and deep neural networks have made progress, but pre-trained language models (PLMs) with limited parameters often produce incorrect SQL as databases and user questions become more complex. LLMs, which show significant capabilities in natural language understanding as model scale increases, offer opportunities to improve text-to-SQL research. The paper provides a comprehensive review of existing LLM-based text-to-SQL studies, including technical challenges, evolutionary process, datasets, metrics, advances, and future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models can help with something called “text-to-SQL”. This means taking user questions written in natural language and turning them into SQL code. Right now, this task is hard because it involves understanding what the user is asking, figuring out what kind of database they’re talking about, and then writing the correct SQL code. Some earlier approaches tried to solve this problem by combining human engineering with deep learning, but these models often got the SQL wrong when the databases or questions got more complex. The paper says that bigger language models might be able to do better because they get better at understanding natural language as they get larger. |
Keywords
» Artificial intelligence » Deep learning » Language understanding » Large language model
