Summary of Lucy: Think and Reason to Solve Text-to-sql, by Nina Narodytska et al.
Lucy: Think and Reason to Solve Text-to-SQL
by Nina Narodytska, Shay Vargaftik
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposes a novel approach to help Large Language Models (LLMs) efficiently query large enterprise databases by combining their strengths with automated reasoning techniques. The authors analyze the limitations of LLMs in these settings, where they struggle to reason about complex relationships between tables. To address this challenge, they develop a new framework that leverages LLMs’ ability to understand natural language queries while handling database constraints. This framework outperforms state-of-the-art methods on zero-shot text-to-SQL tasks on complex benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand questions about big company databases. Right now, these computers (called Large Language Models) are great at answering simple questions but get stuck when dealing with many tables and complicated relationships. The researchers figured out why this happens and created a new way to combine the strengths of these computers with special reasoning tools. This new approach is really good at translating text into database queries for complex problems. |
Keywords
» Artificial intelligence » Zero shot
