Summary of Select-sql: Self-correcting Ensemble Chain-of-thought For Text-to-sql, by Ke Shen et al.
SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL
by Ke Shen, Mayank Kejriwal
First submitted to arxiv on: 16 Sep 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 Recent advancements in Text-to-SQL, a critical problem at the intersection of natural language processing and data management research, have showcased the potential of large language models (LLMs) for off-the-shelf performance. However, these models still fall short of expected expert-level performance, particularly when nuanced understanding is required to convert questions to formal SQL queries. To bridge this gap, we propose SelECT-SQL, a novel in-context learning solution that leverages chain-of-thought prompting, self-correction, and ensemble methods. By utilizing GPT-3.5-Turbo as the base LLM, SelECT-SQL achieves a state-of-the-art result on challenging Text-to-SQL benchmarks, outperforming both baseline GPT-3.5-Turbo-based solutions (81.1%) and the peak performance of GPT-4 (83.5%) reported on the Spider leaderboard’s development set with an impressive 84.2% execution accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine asking a computer to answer questions about data, but the computer needs help understanding what you’re saying. This problem is called Text-to-SQL, and it’s important for managing data effectively. Right now, computers are good at answering simple questions, but they struggle with more complex ones that require deep understanding of databases and SQL code. Our new approach, SelECT-SQL, uses a combination of techniques to help computers better understand what we’re asking them. By using this method, we were able to get the computer to answer 84.2% of challenging questions correctly, which is much better than previous methods. |
Keywords
» Artificial intelligence » Gpt » Natural language processing » Prompting
