Summary of From Natural Language to Sql: Review Of Llm-based Text-to-sql Systems, by Ali Mohammadjafari et al.
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems
by Ali Mohammadjafari, Anthony S. Maida, Raju Gottumukkala
First submitted to arxiv on: 1 Oct 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 This paper investigates the application of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) in natural language processing. Specifically, it explores how these models can be used to translate SQL queries from natural language inputs. The survey covers the evolution of LLM-based text-to-SQL systems, from early rule-based approaches to advanced LLM methods that utilize RAG systems. The paper discusses benchmarks, evaluation methods, and metrics, as well as novel applications such as Graph RAGs for improved contextual accuracy and schema linking. Key challenges addressed include computational efficiency, model robustness, and data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how computers can understand what people mean when they ask questions about databases in plain language. The scientists studied how Large Language Models (LLMs) work with a technique called Retrieval Augmented Generation (RAG) to turn those natural language questions into correct database queries. They also looked at new ways to use RAG, like drawing connections between different parts of the query and the database. The team wants to make sure these systems are fast, reliable, and protect people’s private data. |
Keywords
» Artificial intelligence » Natural language processing » Rag » Retrieval augmented generation
