Summary of Chess: Contextual Harnessing For Efficient Sql Synthesis, by Shayan Talaei et al.
CHESS: Contextual Harnessing for Efficient SQL Synthesis
by Shayan Talaei, Mohammadreza Pourreza, Yu-Chen Chang, Azalia Mirhoseini, Amin Saberi
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 introduces a novel Large Language Model (LLM) based multi-agent framework called CHESS for efficient and scalable SQL synthesis. The framework consists of four specialized agents: Information Retriever, Schema Selector, Candidate Generator, and Unit Tester. Each agent targets one of the challenges in translating natural language questions into SQL queries, including handling large database catalogs, schemas, and values. The framework offers configurable features that adapt to various deployment constraints, such as supporting industrial-scale databases, achieving state-of-the-art privacy-preserving performance, and scaling with additional compute budget. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a system called CHESS that can translate natural language questions into SQL queries. It’s like having a super smart computer assistant that can understand what you’re asking and give you the answer in a way that makes sense to you. The system is made up of four parts: one that finds the right information, one that helps narrow down big databases, one that comes up with good answers, and one that checks those answers to make sure they’re correct. This system can help people who need to work with big databases by making it easier for them to get the answers they need. |
Keywords
» Artificial intelligence » Large language model
