Summary of Pet-sql: a Prompt-enhanced Two-round Refinement Of Text-to-sql with Cross-consistency, by Zhishuai Li et al.
PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency
by Zhishuai Li, Xiang Wang, Jingjing Zhao, Sun Yang, Guoqing Du, Xiaoru Hu, Bin Zhang, Yuxiao Ye, Ziyue Li, Rui Zhao, Hangyu Mao
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed two-stage framework enhances the performance of large language models (LLMs) in generating SQL queries from natural language input. The framework consists of a novel prompt representation, called reference-enhanced representation, which includes schema information and randomly sampled cell values to instruct LLMs. This is followed by a first stage that retrieves question-SQL pairs as few-shot demonstrations, prompting the LLM to generate a preliminary SQL (PreSQL). The second stage simplifies the prompt’s schema information and instructs the LLM to produce the final SQL. As a post-refinement module, cross-consistency across different LLMs is proposed instead of self-consistency within a particular LLM. This approach achieves state-of-the-art (SOTA) results on the Spider benchmark with an execution accuracy of 87.6%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to help computers understand natural language and generate SQL code. The method uses a special type of computer model called a large language model, which is trained to understand human language. The approach involves two stages: first, the model generates a preliminary SQL query based on some examples, and then it refines this query by linking it to relevant information in the database. This results in more accurate and efficient generation of SQL code. |
Keywords
» Artificial intelligence » Few shot » Large language model » Prompt » Prompting
